Patent Publication Number: US-2002013664-A1

Title: Rotating equipment diagnostic system and adaptive controller

Description:
CROSS REFERENCE  
     [0001] This application claims the benefit of U.S. Provisional Application No. 60/212,392 filed Jun. 19, 2000. 
    
    
     
       FIELD OF THE INVENTION  
       [0002] The present invention relates to process control and process monitoring, particularly to control and monitoring of rotating equipment through the use of machine status classification where, in one embodiment, adaptive control measures responsive to the machine status are implemented.  
       BACKGROUND  
       [0003] As automation of production facilities and manufacturing processes has progressed, the number of human operators working with consistent attention to machines used in those facilities and processes has decreased; in compensating for this diminishing intimate involvement of operating technicians with the machines, quality control and quality assurance monitoring with computers programmed to mirror human logical and intuitive understanding has gained importance. Automatic diagnostic systems utilize pattern recognition, embedded rules, and functional relationships to characterize measurements of the monitored machine in operation; and a human expert frequently is involved in helping to interpret the measurements. Expert rule sets, classifiers, neural network-based analysis, and fuzzy-logic systems are gradually extending the productivity of human experts in providing automated systems which can generate routine feedback and status determination. As one example of a product in this area, Bently Nevada has developed  Machine Condition Manager™  2000 (Machine Condition Manager is a trademark of Bently Nevada Corporation) using Gensym Corporation&#39;s G2™ (G2 is a trademark of Gensym Corporation) product.  
       [0004] An earlier important publication in this area of technology was the Dissertation “Classification of Vibration Signals By Methods of Fuzzy Pattern Recognition” (Klassifikation von Schwingungssignalen mit Methoden der unscharfen Mustererkennung”) by Dr. J. Strackeljan (a named inventor in this application) on Jun. 4, 1993 at the Technical University of Clausthal; this publication is incorporated herein by reference. The work describes an approach and a formalized methodology for a feature extraction process and classification algorithm as a basic element in a new type of integrated system for machine diagnosis and machine operation decision support. Other earlier feature selection publications of note are:  
       [0005] Chang, C., “Dynamic Programming as applied to Feature Subset Selection in a Pattern Recognition System”, IEEE Transactions on Systems, Man and Cybernetics, 1973, No. 3, S.166-171;  
       [0006] Chien, Y. T., “Selection and Ordering of Feature Observations in a Pattern Recognition System”, Information and Control, 1968, No. 12, pp.394-414;  
       [0007] Fu, K. S., “Sequential Methods in Pattern Recognition and Machine learning”, Academic Press, New York, 1968;  
       [0008] Fukunaga, K., “Repression of Random Processes using finite Karhuen-Loewe-Expansion”, Information and Control, Vol. 16, 1970, S. 85--101; and  
       [0009] Fukunaga, K., “Systematic Feature Extraction”, IEEE Transactions on pattern Analyses, Nr. 3, 1982.  
       [0010] One of the needs in use of classification systems relates to handling of anomalous measurements which do not initially appear to belong to any predefined status class. There is also a need for a machine diagnosis system which can be configured to diagnose a particular machine within a few days of the date of installation of the machine. Another emerging need in the art is for an approach which assimilates very large classification feature sets as the number of sensors (and the affiliated number of derived classification features) which can be simultaneously monitored by one CPU continues to increase. There is also an ongoing need for new feature types so that the diagnostic facility of the systems is rendered from an ever-improving datalogical reference frame. The Strackeljan Dissertation describes an approach for rapidly and efficiently resolving a large number of predictive features into a usefully defined subset of those features; this efficient approach is valuable in providing a basis for a system which can adapt its learning set in response to anomalous measurements even as it continues to provide real-time classification services. The present invention incorporates the approach described in the Strackeljan thesis along with further developments in providing solutions to all of the above-identified needs.  
       [0011] Further features and details of the invention are appreciated from a study of the Figures and Detailed Description of the Preferred Embodiments.  
       SUMMARY OF THE INVENTION  
       [0012] The invention provides a computer-implemented method for monitoring a sensor and related machine component in a mechanical component assembly, through:  
       [0013] providing a predetermined set of candidate data features for classifying said sensor respective to at least two defined classes;  
       [0014] measuring in real-time an input signal from the sensor;  
       [0015] determining a first computer-determined class affiliation parameter value for the input signal from the candidate data feature set in reference to a first classifying parameter set respective to a first class;  
       [0016] determining a second computer-determined class affiliation parameter value for the input signal from the candidate data feature set in reference to a second classifying parameter set respective to a second class;  
       [0017] deriving, during the real-time measuring and determining steps, a third classifying parameter set for the input signal respective to the first class and a fourth classifying parameter set for the input signal respective to the second class when all computer-determined class affiliation parameter values respective to an input signal measurement in real-time have a quantity less than a predetermined threshold value, the third and fourth classifying parameter sets incorporating the influence of the input signal measurement; and  
       [0018] replacing the first and second classifying parameter sets respectively with the third and fourth classifying parameter sets so that the third and fourth classifying parameter sets respectively become the new first and second classifying parameter sets when the third and fourth classifying parameter sets have been derived.  
       [0019] The invention also provides a dimensionless peak amplitude data feature and a dimensionless peak separation data feature for use in classifying. An organized datalogical toolbox for operational component status classification is also provided.  
       [0020] Other features, advantages, and benefits of the invention are readily apparent from the detailed description of the preferred embodiments when taken in connection with the accompanying drawings. 
     
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
     [0021]FIG. 1 presents a block diagram of the monitoring system and auxiliary systems as they operate and monitor a manufacturing apparatus.  
     [0022]FIG. 2 shows detail in the galvanic isolation and signal filtering board.  
     [0023]FIG. 3 shows the band pass filter circuit used on the galvanic isolation and signal filtering board.  
     [0024]FIG. 4 presents a block flow overview of key logical components of the monitoring system.  
     [0025]FIG. 5 presents a block flow overview of signal conditioning logical components of the monitoring system.  
     [0026]FIG. 6 presents a block flow diagram of the real-time executive logic in the monitoring system.  
     [0027]FIG. 7 presents detail of functions performed at the direction of the real-time control block.  
     [0028]FIG. 8 presents a block flow diagram of the human interface logic in the monitoring system.  
     [0029]FIGS. 9A and 9B present a block flow diagram of the pattern recognition logic in the monitoring system.  
     [0030]FIG. 10 presents detail in a decision function set of the pattern recognition logic.  
     [0031]FIG. 11 presents a block flow diagram of the signal and data I/O and logging logic in the monitoring system.  
     [0032]FIG. 12 presents detail in the tool-specific feature derivation functions.  
     [0033]FIG. 13 presents a block flow diagram of the reference data logic in the monitoring system.  
     [0034]FIG. 14 presents details for a machine analysis toolbox.  
     [0035]FIG. 15 presents an overview flowchart of the organization of key information in constructing and using preferred embodiments.  
     [0036]FIG. 16 presents a flowchart of key classification steps.  
     [0037]FIG. 17 presents a flowchart detailing decisions in use of progressive feature selection, evolutionary feature selection, neural network classification, and weighted distance classification.  
     [0038]FIG. 18 presents detail in the weighted distance method of classifying and progressive feature selection.  
     [0039]FIG. 19 illustrates auxiliary detail in the progressive feature selection process of FIG. 18.  
     [0040]FIG. 20 presents detail in the neural network method of classifying and in evolutionary feature selection.  
     [0041] FIGS.  21 A- 21 D illustrate detail in an evolutionary feature selection example.  
     [0042]FIG. 22 presents an overview of interactive methods and data schema in the preferred embodiments for use of the weighted distance classification method and a progressive feature selection methodology.  
     [0043]FIG. 23 presents an overview of interactive methods and data schema in the preferred embodiments for use of the neural network classification method and an evolutionary feature selection methodology.  
     [0044]FIG. 24 presents a unified mechanical assembly of machine components and attached sensors.  
     [0045]FIG. 25 presents a block flow summary showing toolbox development information flow for a particular set of unified mechanical assemblies and machine components.  
     [0046]FIG. 26 presents a view of key logical components, connections, and information flows in use of the monitoring system in a monitoring use of the preferred embodiment.  
     [0047]FIG. 27 presents a view of key logical components, connections, and information flows in use of the monitoring system in an adaptive control use of the preferred embodiment.  
     [0048]FIG. 28 shows an example of a graphical icon depiction of class affiliation parameter values in normalized form.  
     [0049]FIG. 29 shows an example of a graphical icon depiction of class affiliation parameter values in non-normalized form.  
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
     [0050] In describing the preferred embodiments, a number of “logical engines” (“engines”) are characterized in interaction with data structural elements. In this regard, computer-implemented logical engines generally reference virtual functional elements within the logic of a computer which primarily perform tasks which read data, write data, calculate data, and perform decision operations related to data. “Logical engines” (“engines”) optionally provide some limited data storage related to indicators, counters, and pointers, but most data storage within computer-implemented logic is facilitated within data structural elements (data schema) which hold data and information related to the use of the logic in a specific instance; these data structural element logical sections are frequently termed as “tables”, “databases”, “data sections”, “data commons”, and the like. Data structural elements are primarily dedicated to holding data instead of performing tasks on data and usually contain a generally-identified stored set of information. “Logical engines” (“engines”) within computer-implemented logic usually perform a generally identified function. As a design consideration, the use of both logical engines and logical tools within a logical system enables a useful separation of the logical system into focused or abstracted subcomponents which can each be efficiently considered, designed, studied, and enhanced within a separately focused and distinctively particularized context. As should be apparent, some of the logical internal systems represent distinctive areas of specialty in their own right, even as they are incorporated into the comprehensive and holistic system represented by each of the described embodiments. In one context, specific engines are individual executable files, linked files, and subroutine files which have been compiled into a unified logical entity. Alternatively, specific engines are combinations of individual executable files, linked files, subroutine files, and data files which are datalogically linked either in unified form or in a dynamically associated manner by the operating system during execution.  
     [0051] The specification also references the term “Real-Time” (real-time, real time, Real-time); to facilitate clarity, the following paragraph presents a discussion of the Real-Time concept.  
     [0052] Real-time computer processing is generically defined as a method of computer processing in which an event causes a given reaction within an actual time limit and wherein computer actions are specifically controlled within the context of and by external conditions and actual times. As an associated clarification in the realm of process control, real-time computer-controlled processing relates to the performance of associated process control logical, decision, and quantitative operations intrinsic to a process control decision program functioning to monitor and modify a controlled apparatus implementing a real-time process wherein the process control decision program is periodically executed with fairly high frequency usually having a period of between 10 ms and 2 seconds, although other time periods are also utilized. In the case of “advanced” control routines (such as the classifier of the described embodiments) where a single solution instance requires more extended computational time, a larger period is essentially necessary (frequency in determination of changes in control element settings should be executed at a frequency equal-to-or-less-than the frequency of relevant variable measurement); however, an extended period for resolution of a particular value used in control is still determined in real-time if its period of determination is repetitive on a reasonably predicable basis and is sufficient for utility in adaptive control of the operating mechanical assembly.  
     [0053] A measuring sensor attached to an apparatus usually outputs a voltage or voltage equivalent responsive to an attribute of the operational apparatus (e.g., an open valve or an energized pump) and/or conditions (e.g., fluid temperature or fluid pressure) in the materials operationally processed by the apparatus.  
     [0054] A signal (measured signal) represents the magnitude of the voltage either as a data value at a particular moment of time or, alternatively, as a set of data values where each data value has an explicit or implicit (via sequential ordering) association with a time attribute. The term “signal” in many instances also references the voltage or voltage history as converted to data value representation.  
     [0055] The signal is evaluated in the context of a function to derive specific signal function attributes; these signal attributes are also termed features (Features) both (a) as a descriptive term generally and also (b) as a reference variable in pattern-matching processes such as “classification”. In this regard, Features frequently reference a variable possessing a joining consideration or datalogical nexus between (a) an attribute derived in the context of a function from the measured signal and (b) a variable used in a classifier. A feature value generally represents a particular quantitative data value which has been assigned-to and associated-with a feature variable respective to a signal measurement instance.  
     [0056] Classifiers generally associate features—more specifically, patterns of features—with a membership (association, belonging, and/or affiliation) of the operational apparatus (generating the features) in a particular momentary status of identified useful categorization (a class); in this regard, membership is either (a) a designation, in one context, of belonging to the class or (b) a designation, in an alternative context, of not belonging to the class. Classes frequently are representative of human quality evaluations and/or judgements (e.g. a “good” class, a “bad” class, and/or a “transitional” class which represent, respectively, a “good” state of operational performance, a “bad” state of operational performance, and/or an “uncertain or transitioning” state of operational performance). Membership also references a degree of belonging to a class—e.g. in a two class evaluation, a degree of affiliation with the two classes is characterized as “the current state of the system is 90% ‘good’ and 10% ‘bad’”; more precisely, the concept of “sharpness” further references the quantitative confidence with which a particular classified measurement instance (in the context of its affiliated classifying feature set) is clearly affiliated with any class of the set of candidate classes for which membership is derived.  
     [0057] In classifying, Weighted Distance Classification and Euclidian Distance Classification reference certain overlapping situations; accordingly, references to Weighted Distance Classification herein implicitly includes appropriate use of Euclidian Distance Classification in the context of these similarities. In this regard, classification performance strongly depends on the ability of a particular classifier to adapt the distribution of a particular learning sample in an optimal manner. If a set of learning samples is represented in an essentially spherical distribution for all classes, the Euclidian metric is sometimes used. If the distribution is ellipsoidal, Weighted Distance approaches are optimal in coordinate directions weighted individually. In this regard, marginal samples are appraised similarly respective to different Euclidian distances. Essentially, the Euclidian metric is a special form of a Weighted Distance metric (when the weights are essentially equal for all directions); the inventors prefer, therefore, the use of a weighted distance classifier in general.  
     [0058] Turning now to the figures, FIG. 1 presents a block diagram of the monitoring system and auxiliary systems as they operate and monitor a manufacturing apparatus. System Overview  100  presents key physical components in an fully applied embodiment. Monitor  102  provides a monitor for human (operator technician and configuration expert) viewing of information and data. Process Information System  104  provides a process information system (a system for retaining and depicting information to operating technicians about data executing in an affiliated, attached, and interconnected real-time control system or group of real-time control systems but which is not under the highly rigorous real-time response cadence of a real-time control system for its communications) in bilateral data communication via Communications Interface  106  with Control Computer  108 . Process Information System  104  incorporates Process Information CPU  134  for execution of Process Information Logic  136 . Communications Interface  106  incorporates Communication Interface CPU  130  for execution of Communication Interface Logic  132 . Control Computer  108  incorporates Control Computer CPU  126  for execution of Control Computer Logic  128  in real-time operational monitoring and control of Mechanical Assembly  124 . Classification Computer System  110  provides Classification Computer CPU  138  for executing Classification Computer Logic  140  in implementing classification of the status of Mechanical Assembly  124 . System Overview  100  is in bilateral data communication with Process Information System  104  for receiving a portion of input data as a data stream and for communicating the classification status of Mechanical Assembly  124  to Control Computer  108  so that Control Computer  108  controls Mechanical Assembly  124  in adaptive response to the classified status. Classification Computer System  110  also receives input data from Analog Input Signal  118  and Digital Input Signal  116  via Signal Filtering Board  114  and Data Acquisition Board  112 . Data Acquisition Board  112  incorporates Analog-to-Digital-Converter Circuit  142  to effect conversion of analog voltages from Signal Filtering Board  114  into digital data. Signal Filtering Board  114  incorporates Band-Pass-Filter Circuit  144  as further described in Filter Circuit Components  200  and Filter Circuit  300  of FIGS. 2 and 3. Digital Input Signal  116  is provided both as a direct signal to Signal Filtering Board  114  and to Control Signal Input Circuitry  148  where Control Signal Input Circuitry  148  is synchronous with the needs of Control Computer  108 . Analog Input Signal  118  is provided both as a direct signal to Signal Filtering Board  114  and to Control Signal Input Circuitry  148  where Control Signal Input Circuitry  148  is appropriately synchronous with Control Computer  108 . Digital Output Signal  120  and Analog Output Signal  122  provide output command signals from Control Signal Output Circuitry  150  to Mechanical Assembly  124  so that Control Computer  108  implements manipulated variables to modify attributes of Mechanical Assembly  124  and thereby control the operation of Mechanical Assembly  124  in real-time. An example of Control Computer  108  is described in WO Publication No. 00/65415, dated Nov. 2, 2000, entitled “PROCESS CONTROL SYSTEM WITH INTEGRATED SAFETY CONTROL SYSTEM”, which is incorporated herein by reference.  
     [0059] Mechanical Assembly  124  is a mechanical component assembly, which benefits from Classification Computer System  110  (1) by the provision of information to an operating technician of the classified status of the operating assembly and (2), optionally, by the incorporation of the classified status into control decisions effected by Control Computer Logic  128 . The classified status is communicated to Control Computer Logic  128  via Process Information System  104  and Communications Interface  106 . Mechanical Assembly  124  is, alternatively, in example and without limitation, a motor, a gearbox, a centrifuge, a steam turbine, a gas turbine, a gas turbine operating with the benefit of wet compression, a chemical process, an internal combustion engine, a wheel, a furnace, a transmission, or an axle. With respect to wet compression, U.S. Pat. No. 5,867,977 for a “Method and Apparatus for Achieving Power Augmentation in Gas Turbines via Wet Compression” which issued on Feb. 9, 1999 to Richard Zachary and Roger Hudson and also U.S. Pat. No. 5,930,990 which issued on Aug. 3, 1999 to the same inventors provide a useful teaching of a gas turbine operating with the benefit of wet compression; these two patents are incorporated herein by reference.  
     [0060] Network  146  is in bilateral data communication with Classification Computer System  110  and provides an interface via network with other systems. In an alternative embodiment, Process Information System  104  interfaces with Classification Computer System  110  via Network  146 ; in a further alternative embodiment, Communications Interface  106  interfaces with Classification Computer System  110  via Network  146 . Control Signal Input Circuitry  148  generically references a set of circuits which are respectively specific to Digital Input Signal  116  and Analog Input Signal  118  in interfacing to Control Computer  108 .  
     [0061] Details in Process Information System  104 , Communications Interface  106 , Control Computer  108 , Network  146 , and Data Acquisition Board  112  should be apparent to those of skill and are presented here briefly to enable a framed understanding of preferred embodiments and their use. Details in Classification Computer Logic  140  and Signal Filtering Board  114  are focal in most subsequent discussion in this specification.  
     [0062]FIG. 2 shows detail in the galvanic isolation and signal filtering board. Filter Circuit Components  200  shows further detail in Signal Filtering Board  114 . Frequency Module  202  presents construction details in Frequency Modules  206 . Band-Pass-Filter Circuitry Board  204  shows an embodiment of Signal Filtering Board  114  with a set of Frequency Modules  206 , a set of Transformers  208 , and a set of Input Capacitors  210  in electrical mounting as shown. As previously noted, a instance of Frequency Modules  206  is further detailed in Frequency Module  202  which is provided in 5 separate instances on Band-Pass-Filter Circuitry Board  204 . Transformer  208  is provided in 5 separate instances on Band-Pass-Filter Circuitry Board  204 . Input Capacitors  210  are also provided in 5 separate instances on Band-Pass-Filter Circuitry Board  204 . Signal Wire Terminators  212  provide 5 separate wiring terminations for use in interfacing 5 separate instances of Analog Input Signal  118  to Data Acquisition Board  112 . It should be noted that Digital Input Signal  116  is optionally routed in a pass-through manner to Classification Computer System  110  via Signal Filtering Board  114  and Data Acquisition Board  112 , but most signals used by Classification Computer System  110  are of the Analog Input Signal  118  type. Frequency Capacitor “a”  214 , Frequency Capacitor “b”  218 , and Frequency Capacitor “c”  222  provide respective first, second, and third capacitors in Frequency Module  202 . Frequency Inductor “a”  216  and Frequency Inductor “b”  220  provide respective first and second inductors in Frequency Module  202 .  
     [0063]FIG. 3 shows the band pass filter circuit used on the galvanic isolation and signal filtering board. Filter Circuit  300  shows one band pass filter circuit which is established by the combination of Input Capacitors  210  instance C 1 , Transformers  208  instance T 1 , and Frequency Modules  206  instance M 1  with C a1  mapping to Frequency Capacitor “a”  214 , L a1  mapping to Frequency Inductor “a”  216 , C b1  mapping to Frequency Capacitor “b”  218 , L b1  mapping to Frequency Inductor “b”  220 , and C c1  mapping to Frequency Capacitor “c”  222 . These are preferably characterized according to the following criteria of Table 1:  
                   TABLE 1                       Upper           cut-off frequency: f g  = 2 Khz   Upper Cut-off Frequency f g  = 20 Khz                                                        C210   10 μF/100 V   L a  47 μH   C210   10 μF/100 V   L a  47 μH       C a     330 nF/100 V   L b  47 μH   C a     47 nF/100 V   L b  47 μH       C b     330 nF/100 V       C b     47 nF/100 V       C c     330 nF/100 V       C c     47 nF/100 V                     T208   ST 6353 (signal transformer)                          
 
     [0064] In one embodiment having two instances of Band-Pass-Filter Circuitry Board  204 , a beneficial arrangement of Band-Pass-Filter Circuit  144  instances is shown in Table 2.  
               TABLE 2                          Band-Pass-Filter Circuit 144 Configuration                             I/O               Channel               212   Frequency                                             S0   20   Khz           S1   20   Khz           S2   20   Khz           S3   2   Khz           S4   2   Khz           S5   20   Khz           S6   20   Khz           S7   20   Khz           S8   2   Khz           S9   2   Khz                      
 
     [0065]FIG. 4 presents a block flow overview of key logical components of the monitoring system. Classifying Logic  400  provides a first nested opening of Classification Computer Logic  140 . Real-Time Executive Logic  402  is in bilateral data communication with Reference Data Logic  404 , Human Interface Logic  412 , Pattern Recognition Logic  406 , and Signal I/O Logic  408  and is further discussed with respect to Real-Time Logic Detail  600  and Real-Time Function Detail  700  of FIGS. 6 and 7. As should be apparent, Real-Time Executive Logic  402  provides execution enablement data signals and multi-process and/or multitasking interrupts to all engines and other executable logic of Reference Data Logic  404 , Human Interface Logic  412 , Pattern Recognition Logic  406 , and Signal I/O Logic  408  as needed and receives feedback and flagging inputs so that responsive logic is executed in a unified and coordinated real-time cadence. Reference Data Logic  404  also is in bilateral data communication with Human Interface Logic  412  and Pattern Recognition Logic  406  and is further discussed with respect to Reference Data Detail  1300  and Toolbox  1400  of FIGS. 13 and 14. Pattern Recognition Logic  406  also is in bilateral data communication with Signal I/O Logic  408  and Human Interface Logic  412  and is further discussed with respect to Pattern Recognition Logic Detail  900  and Decision Function Detail  1000  of FIGS. 9A, 9B, and  10 . Signal I/O Logic  408  also is in bilateral data communication with Human Interface Logic  412  and is in data reading communication with Signal Conditioning Logic  410  and is further discussed with respect to Signal Logic Detail  1100  and Derivation Functions  1200  of FIGS. 11 and 12. Signal Conditioning Logic  410  reads Analog Input Signal  118  and Digital Input Signal  116  and provides values via read access to Signal I/O Logic  408 ; this logical section is further discussed respective to Signal Conditioning Detail  500  of FIG. 5. Human Interface Logic  412  interfaces to Monitor  102  to provide an interface with operating technicians; this logic is further detailed in the discussion respective to Interface Logic Detail  800  of FIG. 8.  
     [0066]FIG. 5 presents a block flow overview of signal conditioning logical components of the monitoring system. Signal Conditioning Detail  500  provides further detail in Signal Conditioning Logic  410  and also reprises Signal I/O Logic  408  along with Analog Input Signal  118  and Digital Input Signal  116  for reference. Analog Signal Input Buffer  504  holds data from Analog Value Input Logic  510  so that Signal I/O Logic  408  can read the data in a timely manner. Digital Signal Input Buffer  506  holds data from Digital Value Input Logic  508  so that Signal I/O Logic  408  can read the data in a timely manner. Digital Value Input Logic  508  provides a logical engine for real-time acquisition of Digital Input Signal  116  and interface of Digital Input Signal  116  to Digital Signal Input Buffer  506 . It is again noted that use of Digital Input Signal  116  is relatively minimal at this time in the described embodiments, but use of Digital Input Signal  116  signals is certainly possible in certain contemplated circumstances (e.g., without limitation, a machine “trip” indicator). The Analog Value Input Logic  510  engine provides logic necessary for real-time operation of Analog-to-Digital-Converter Circuit  142  and interface of Analog Input Signal  118  to Analog Signal Input Buffer  504 .  
     [0067]FIG. 6 presents a block flow diagram of the real-time executive logic in the monitoring system. Real-Time Logic Detail  600  provides further detail in Real-Time Executive Logic  402  and also reprises Reference Data Logic  404 , Pattern Recognition Logic  406 , Human Interface Logic  412 , and Signal I/O Logic  408  for reference. Real-Time Executive Engine  602  contains Control Block  604  for providing cadenced execution of Classification Computer Logic  140 . In this regard, Control Block  604  contains sub-logic for substantially directing Classification Computer CPU  138  to implement Classifying Logic  400  in achieving the goals of the classifying system using either multi-process or multi-tasking approaches. Control Block  604  interfaces with routines in Function Set  606  in implementation of Classification Computer Logic  140 . Further detail in Function Set  606  is presented in the discussion with respect to Real-Time Function Detail  700  of FIG. 7. Control Block  604  is also responsive to status indicators as indicated in Mode ID  608 . The “Configure”, “Learn”, and “Run” modes of operation are defined in one embodiment via input from Human Interface Logic  412  with human designation of the particular active mode at any particular time.  
     [0068]FIG. 7 presents detail of functions performed by use of the real-time control block. Real-Time Function Detail  700  shows further detail in Function Set  606 . In this regard, the internal functions of Function Set  606  are in bilateral data communication (i.e., data read communication and data write communication in both directions as appropriate) with Control Block  604 . Hardware Configuration Function  702  provides code in interfacing Human Interface Logic  412  to Signal I/O Logic  408  for configuring Classification Computer System  110  to a particular set of Analog Input Signals  118  and Digital Input Signals  116 . Sample Collection Function  704  provides code in interfacing Human Interface Logic  412  and Signal I/O Logic  408  in acquiring sample data for use in customizing System Overview  100  to a particular Mechanical Assembly  124 . Database Acquisition Function  706  provides code in interfacing Human Interface Logic  412  and Reference Data Logic  404  to load learning databases into system  110 . Tool Selection Function  708  provides code in interfacing Human Interface Logic  412  and Reference Data Logic  404  to define tools for use with particular signals. Component Selection Function  710  provides code in interfacing Human Interface Logic  412  and Reference Data Logic  404  in defining components which can then define tools. Feature Calculation Function  712  provides code in interfacing Reference Data Logic  404  and Signal I/O Logic  408  to calculate features for use in Pattern Recognition Logic  406 . Feature Selection Function  714  provides code in interfacing Reference Data Logic  404  and Pattern Recognition Logic  406  in selecting features for classification use. Learning Function  716  provides code in interfacing Reference Data Logic  404 , Human Interface Logic  412 , and Pattern Recognition Logic  406  in implementing a learning process to acquire a learning database. Classifier Definition Function  718  provides code in interfacing Reference Data Logic  404 , Human Interface Logic  412 , and Pattern Recognition Logic  406  in defining a classifier. Real-Time Characterization Function  720  provides code in interfacing Reference Data Logic  404 , Signal I/O Logic  408 , Pattern Recognition Logic  406 , and Human Interface Logic  412  in implementing real-time membership value determinations to classify Mechanical Assembly  124  in operation. Adaptation Function  722  provides code in interfacing Human Interface Logic  412 , Reference Data Logic  404 , Pattern Recognition Logic  406 , and Signal I/O Logic  408  in implementing adaptation of the classifying system in real-time to assimilate learning related to measured signals or data which are not classifiable to an acceptable confidence with the existing classifier. Network Interfacing Function  724  provides code in interfacing Signal I/O Logic  408  and Human Interface Logic  412  with Network  146  or Process Information System  104 . Display Function  726  provides code in interfacing Signal I/O Logic  408  and Human Interface Logic  412  and further in interfacing Human Interface Logic  412  and Monitor  102  so that an operating technician is apprised of the classification status of Mechanical Assembly  124  in operation.  
     [0069]FIG. 8 presents a block flow diagram of the human interface logic in the monitoring system. Interface Logic Detail  800  presents expanded detail of Human Interface Logic  412 . Real-Time Executive Logic  402 , Reference Data Logic  404 , Signal I/O Logic  408 , and Pattern Recognition Logic  406  are reprised from FIG. 4. Graphical Output Engine  802  is in bilateral data communication with Real-Time Executive Logic  402  for (1) data write communicating the occurrence of anomalous measured vectors (to Adaptation Function  722 ) as determined by Rework Engine  810  (and communicated from Associative Value Engine  812 ), (2) data read communication from functions in Function Set  606  which output information to the operating technician, and (3) receipt of multi-process and/or multitasking interrupts and execution enablement data signals from Real-Time Executive Logic  402 . Graphical Output Engine  802  is in data reading communication with Signal I/O Logic  408 , Reference Data Logic  404 , and Associative Value Engine  812  so that data from these sections is output to the operating technician. Graphical Input Engine  804  interfaces the keyboard or other input device associated with Monitor  102  in bilateral data communication with Real-Time Executive Logic  402  for execution-enablement data signals, multi-process and/or multitasking interrupts, and data input to Function Set  606  and Mode ID  608 . Graphical Input Engine  804  is in data writing communication with Reference Data Logic  404 , Pattern Recognition Logic  406 , and Characterization Selection Routine  806  so that data is input from the operating technician to these logical sections as needed. Graphical Input Engine  804  also is in bilateral data communication with Learning Data Loading Engine  808  to facilitate operating technician activation of loading of learning database data and toolbox data (discussion with respect to FIGS. 13 and 14) into Signal I/O Logic  408  and Reference Data Logic  404 . Graphical Input Engine  804  optionally contains Input Function Set  814  for enabling particular data sets to be defined as a group for communication in a unified data write operation. Characterization Selection Routine  806  is in data reading communication with Graphical Input Engine  804  and is in data writing communication with Pattern Recognition Logic  406  to enable operating technician selection of either a Neural Network or Weighted Distance Classifier for use in classification. Learning Data Loading Engine  808  interfaces to Signal I/O Logic  408  for networked data or to a disk or CD-ROM (not shown) in Classification Computer System  110  in loading of learning database data and toolbox data into Signal I/O Logic  408  and Reference Data Logic  404 . Rework Engine  810  is in bilateral data communication with Associative Value Engine  812  in evaluating memberships determined in Associative Value Engine  812  as part of identifying anomalous measured vectors and notifying Real-Time Executive Logic  402  as described above. Rework Engine  810  also is in data writing communication with Signal I/O Logic  408  for flagging retention of anomalous measurements to the attention of the operating technician. Associative Value Engine  812  is in data reading communication with Signal I/O Logic  408  for receiving membership values and determining appropriate membership value display data (e.g., without limitation, basic or normalized form). Associative Value Engine  812  is in bilateral data communication with Rework Engine  810  and is in data writing communication with Graphical Output Engine  802  for purposes previously discussed.  
     [0070]FIGS. 9A and 9B present a block flow diagram of the pattern recognition logic in the monitoring system. Pattern Recognition Logic Detail  900  presents detail in Pattern Recognition Logic  406 . Signal I/O Logic  408 , Reference Data Logic  404 , Real-Time Executive Logic  402 , and Human Interface Logic  412  are reprised from FIG. 4. Evolutionary Feature Selector  902  is in bilateral data communication with Reference Data Logic  404  for receiving learned data and toolbox data (FIGS. 13 and 14) needed in defining a set of features for use in classification. Evolutionary Feature Selector  902  implements random selection of a plurality of feature sets where each individual set of features is then used by Weighted Distance Classifier  906  or Neural Net Engine  908  in defining a classifier; the classifier is then used to evaluate the memberships of individual test measurements; the evaluations are then compared to judgments from a human expert to define the most acceptable sets of features in the plurality of feature sets. The most acceptable feature sets are then either enhanced or randomly cross-mutated (FIGS.  21 A- 21 D) on a feature-by-feature basis to define a new plurality of feature sets. When an acceptable threshold of classification confidence is achieved, the feature set achieving the threshold is then used to classify Mechanical Assembly  124 . A further discussion of the evolutionary operation of Evolutionary Feature Selector  902  is presented in the discussions of Evolutionary Feature Selection Process  1900  of FIG. 20 and in the Example illustrated by FIGS.  21 A- 21 D. Evolutionary Feature Selector  902  is in bilateral data communication with Selected Feature Stack  910  to store most acceptable feature sets; Evolutionary Feature Selector  902  is in bilateral data communication with Neural Net Engine  908  and Weighted Distance Classifier  906  for classifying feature sets and evaluating results. Evolutionary Feature Selector  902  is in data reading communication with neural network Parameter Instance  912  and in data writing communication with NN Real-Time Parameters  914  for reading and storing the final selected set of features and classification reference parameters (weighting matrix and adaptation parameters) for real-time use. As should also be apparent, Evolutionary Feature Selector  902  is in bilateral data communication with Real-Time Executive Logic  402  for execution enablement data signals, multi-process and/or multitasking interrupts, and data input to Function Set  606 .  
     [0071] Progressive Feature Selector  904  is in bilateral data communication with Reference Data Logic  404  for receiving learned data and other toolbox data needed in defining a set of features for use in classification. Progressive Feature Selector  904  implements a routine of progressively evaluating an iteratively decreased plurality of feature sets where each set of features is used by Weighted Distance Classifier  906  or Neural Net Engine  908  in defining a classifier; the classifier is then used to evaluate the memberships of individual test measurements; and the evaluations are then compared to judgments from a human expert to define the most acceptable sets of features in the plurality of feature sets. The features of the most acceptable feature set are then enhanced with features not in the acceptable set to define a new plurality of feature sets. When an acceptable threshold of classification confidence is achieved, the feature set achieving the threshold is then used to classify Mechanical Assembly  124 . A further discussion of the progressive selection operation of Progressive Feature Selector  904  is presented in discussion of Progressive Feature Selection Process  1800  of FIG. 18 and in auxiliary detail in FIG. 19. Progressive Feature Selector  904  is in bilateral data communication with Selected Feature Stack  910  to stack the most acceptable features during the process of evaluation; the stacking enables efficient use of memory in retaining the desired features. Progressive Feature Selector  904  is in bilateral data communication with Neural Net Engine  908  and Weighted Distance Classifier  906  for classifying feature sets and evaluating results. Progressive Feature Selector  904  is in data writing communication with Weighted Distance Real-Time Parameters  916  for storing the final selected set of features and classification reference parameters (decision function set and decision feature set) for real-time use. As should also be apparent, Progressive Feature Selector  904  is in bilateral data communication with Real-Time Executive Logic  402  for multi-process and/or multitasking interrupts, execution enablement data signals, and data input to Function Set  606 .  
     [0072] Weighted Distance Classifier  906  is a weighted distance classifier as generally understood in the art. Examples of such classifiers are described in:  
     [0073] Bezdek, J. C., “Pattern Recognition with Fuzzy Objective Function Algorithm”, Plenum Press, New York, 1981;  
     [0074] Gath, I., “Unsupervised Optimal Fuzzy Clustering”, IEEE Trans, Pattern Analysis and Machine Intell., Juli 1989;  
     [0075] Jollife I. T., “Principle Component Analysis”, Springer Verlag 1986;  
     [0076] Kandal, A., “Fuzzy Techniques in Pattern Recognition”, John Wiley, New York, 1982;  
     [0077] Kittler, J., “Mathematical Methods of Feature Selection in Pattern Recognition”, International Journal on Man-Machine Studies, 1975, No. 7, S. 609-637;  
     [0078] Mahalanobis, P. C., “On the generalized distance in statistics”, Proc. Indian Nat. Inst. Sci. Calcutta, 1936, S. 49-55;  
     [0079] Watanabe, S., “Karhuen-Loewe Expansion and Factor Analysis”, Transactions 4th Prague Conference on Information Theory, 1965, S. 635-660;  
     [0080] Zimmermann, H. J., “Fuzzy Set Theory and its Applications”, Kluver Academic Publishers, 1991;  
     [0081] (Previously referenced) Strackeljan, J., “Klassifikation von Schwingungssignalen mit Methoden der unscharfen Mustererkennung”, Dissertation TU Clausthal, 1993; and  
     [0082] Strackeljan, J., Weber, R., “Quality Control and Maintenance”, In: Fuzzy Handbook Prade and Dubois, Vol. 7 Practical Applications of Fuzzy Technologies, Nov. 1999, Kluwer Academic Publisher.  
     [0083] Neural Net Engine  908  is a neural network classifier as generally understood in the art. An example of such a classifier is described in  
     [0084] Rumelhart, D. E., McClelland, J. L. and the PDP Research group, “Parallel Distributed Processing”, MIT Press, Cambridge, MA, 1986  
     [0085] and  
     [0086] Pao, Y. H., “Adaptive Pattern recognition and Neural Networks”, Addison-Wesley Publishing Company, 1989.  
     [0087] All of the above 12 documents are incorporated herein by reference.  
     [0088] In addition to previously discussed data communications, Weighted Distance Classifier  906  and NN Logical Engine  908  are in bilateral data communication with Signal I/O Logic  408  for implementing real-time classification of Mechanical Assembly  124 .  
     [0089] NN (Neural Network) Parameter Instance  912  is in bilateral data communication with Neural Net Engine  908  for holding interim features (real-time Neural Network Feature Set  934 ) and neural network data (Real-Time Weighting Matrix  932 ) during classifier definition. NN Real-Time Parameters  914  provides Weighting Matrix and Adaptation Parameters Instance  928  and Neural Network Feature Set  930  to Neural Net Engine  908  for real-time evaluation of Mechanical Assembly  124 . During adaptation to define a new classifier, NN Real-Time Parameters  914  continues to provide real-time classification of Mechanical Assembly  124  even as Neural Network Parameter Instance  912  is used during the definition of a further improved parameter set for use with Neural Net Engine  908 . Weighted Distance Real-Time Parameters  916  provides Decision Function Set  924  and Decision Feature Set  926  to Weighted Distance Classifier  906  for real-time evaluation of Mechanical Assembly  124 . During adaptation to define a new classifier, Weighted Distance Real-Time Parameters  916  continues to provide real-time classification of Mechanical Assembly  124  even as Weighted Distance Parameter Instance  918  is used during the definition of a further improved parameter set for use with Weighted Distance Classifier  906 . Weighted Distance Parameter Instance  918  is in bilateral data communication with Weighted Distance Classifier  906  for holding interim features (Decision Feature Set  922 ) and Weighted-Distance Classifier data (Decision Function Set  920 ) during classifier definition.  
     [0090] As previously referenced, Selected Feature Stack  910  stacks the most acceptable features during the process of evaluation; the stacking enables efficient use of memory in retaining the desired features. In this regard, the features of the first-evaluated feature sets are automatically retained in the initial feature set until the stack is full; thereafter, features which demonstrate superior classification performance supplant the lower performing features in the stack.  
     [0091] Stack  910  is appreciated in reference to the reclassification rate (predictive capability and/or error) concept. On the basis of a classified learning sample for which an unambiguous class assignment is performed prior to use for each random sample collected during a learning phase, a measure of appraisal is obtained by reclassifying the learning sample with the respective classification algorithm and a selected subset of classifying data. The ratio of (a) the number of random samples correctly classified in accordance with the given class assignment to (b) the total number of random samples investigated provides (c) a measure of the reclassification rate, error, and predictive capability of the particular evaluated classifier and selected classifying data; as should be appreciated, the goal of the process is ultimately to obtain a very small reclassification error. In the ideal case, (a) the decision on class assignment for reclassification agrees with (b) the class subdivision of the learning sample for all objects on the basis of the maximal alignment of the two membership determinations (i.e., the best feature combination is the one that provides the very best alignment between the first determinations of the human expert and the subsequent determinations of the trained classifier respective to the each of the particular feature combinations tested for that alignment). The advantage of the reclassification error concept is the possibility of determining conclusive values even with a small number of random samples.  
     [0092] Separation sharpness is also a key factor in the example. The classification decision gains unambiguity if the distance between the two largest class memberships increases. Based on these membership values, a sharpness factor is defined; the sharpness factor is considered in the selection process if two or more feature combinations have identical classification rates.  
     [0093] Stack  910  is further appreciated in the context of an overview of certain steps used in the method of feature selection.  
     [0094] In Step  1 , the best combinations of features from the totality of all available results are selected (i.e. each feature combination instance is used to train the classifier, classify the sample data of the learning database, generate a comparison between the classified sample and the earlier evaluation of the human expert, and all of the tested feature combination instances thus tested are ranked to define the best predictive feature combinations among all of those combinations evaluated). For this purpose, a sorted list of all calculated measures of quality is prepared; from this list, a specified number of best feature combinations are accepted in a ‘best list’ as a basis for the further selection process.  
     [0095] In step  2 , the best feature combinations of Step  1  (in the first iteration, all feature pairs in the stack; in the next iteration, all feature triplets in the stack; in the nth iteration, all combinations of n+1 features) are successively combined with all features not previously included in the pairing of features. Features for which low measures of quality have been calculated in the appraisal of the feature pairs are thus re-included in the selection process.  
     [0096] In step  3 , the best feature predictor combination is evaluated against a measure of acceptability, and the process of steps  1  and  2  is repeated until (a) one (best) combination with the desired predetermined number of features has been defined or (b) a specified Recall rate (ability to predict vis a vis the human expert) is achieved.  
     [0097] The following example further shows the nature and operation of Selected Feature Stack  910 .  
     EXAMPLE 1  
     [0098] Respective to notation, “z” is the Object number for a particular individual having a feature set and membership in a class (i.e. when z is expressed as a numeric value, then F z,x  is considered to have a specific quantitative value in the example; when z is expressed as the textual “z”, then F z,x  is a logically identified variable representing a classifying feature in the example). An Object, therefore, is a feature vector and affiliated class membership value as a combination.  
     [0099] Beginning with a Feature set size of 2, the example shows Table 3 having 20 samples (10 for class 1 designated with z=1,10, and 10 for class 2 designated with z=11,20) after the set of features has been used to train a classifier and the classifier has been used to categorize each sample in the learning set.  
                           TABLE 3                               Membership Value                   Predicted from using                   trained classifier                   (note: these are examples   Membership           Second   of what the newly-trained   Value Measured       First Feature   Feature   classifier defines as a   from Human       Value   Value   Membership Value set)   Expert Input                  F 1,6      F 1,12      0   0       F 2,6      F 2,12      0   1                   (misclassified)       F 3,6      F 3,12      0   0       F 4,6      F 4,12      0   0       F 5,6      F 5,12      0   0       F 6,6      F 6,12      0   0       F 7,6      F 7,12      0   0       F 8,6      F 8,12      0   0       F 9,6      F 9,12      0   0       F 10,6     F 10,12     0   0       F 11,6     F 11,12     1   1       F 12,6     F 12,12     1   1       F 13,6     F 13,12     1   1       F 14,6     F 14,12     1   1       F 15,6     F 15,12     1   1       F 16,6     F 16,12     1   1       F 17,6     F 17,12     1   1       F 18,6     F 18,12     1   1       F 19,6     F 19,12     1   1       F 20,6     F 20,12     1   1                  
 
     [0100] As can been seen, the Recall Rate=1.0−1.0/20.0=0.95. For each feature combination of 2 features, a Recall Rate is determined. Table 4 shows the F z,6 -F z,12  Recall Rate along with another F z,6 -F z,18  Recall Rate (note that there is no equivalent Table 3 for the F z,6 -F z,18  Recall Rate determination).  
                               TABLE 4                                      F z,6     F z,12     95% correct in                   predicting.           F z,6     F z,18     92% correct in                   predicting.                      
 
     [0101] Table 5 expands on the example of Tables 3 and 4 and adds the Sharpness factor to provide a Sorted list with a stack size of 50.  
                               TABLE 5                           First   Second                   feature   feature   Recall   Sharp-       Pos.   value   value   Rate   ness                  1   6   12   0.95   0.151       2   6   18   0.92   0.125       3   7   14   0.92   0.108       4   6   21   0.91   0.132       5   5   11   0.89   0.095       6   4   12   0.89   0.089       7   6   19   0.88   0.086       8   7   18   0.86   0.084       9   5   34   0.86   0.081       10    5   33   0.85   0.082       .   .   .   .   .       .   .   .   .   .       .   .   .   .   .       48    7   12   0.81   0.071       49    7   33   0.81   0.069       50    6   19   0.80   0.068                  
 
     [0102] Continuing the Example, Table 6 shows a new incoming evaluation:  
                                   TABLE 6                                   First   Second                   feature   feature   Recall   Sharp-           value   value   Rate   ness                          8   14   0.90   0.116                      
 
     [0103] This new F z,8 -F z,14  result of Table 6 pushes part of the Stack  910  down as shown in Table 7 to provide an updated list after evaluation of feature combination  8 | 14 .  
                               TABLE 7                           Feature   Feature   Recall           Pos.   Value 1   Value 2   Rate   Sharpness                  1   6   12   0.95   0.151       2   6   18   0.92   0.125       3   7   14   0.92   0.108       4   6   21   0.91   0.132       5   8   14   0.90   0.116       6   5   11   0.89   0.095       7   4   12   0.89   0.089       8   6   19   0.88   0.086       9   7   18   0.86   0.084       10    5   34   0.86   0.081       .   .   .   .   .       .   .   .   .   .       .   .   .   .   .       48    6   17   0.82   0.081       49    7   12   0.81   0.071       50    7   33   0.81   0.069                  
 
     [0104] End of Example 1  
     [0105]FIG. 10 presents detail in a decision function set of the pattern recognition logic. Decision Function Detail  1000  shows detail in Decision Function Set  920  and Decision Function Set  924 . Each Class used to characterize a measured signal (whether used in classifier definition or in real-time classification) has an affiliated eigenvalue set and eigenvector set. In a system of N Classes being used for classification, Class 1 Eignenvector Set  1002 , Class N Eigenvector Set  1004 , Class 1 Eigenvalue Set  1006 , and Class N Eigenvalue Set  1008  are each retained as shown within Decision Function Set  920  and (for the real-time case) in Decision Feature Set  926 .  
     [0106]FIG. 11 presents a block flow diagram of the signal and data I/O and logging logic in the monitoring system. Signal Logic Detail  1100  therefore presents detail in Signal I/O Logic  408 . Pattern Recognition Logic  406 , Reference Data Logic  404 , Real-Time Executive Logic  402 , Signal Conditioning Logic  410 , and Human Interface Logic  412  are reprised from FIG. 4. Feature Derivation Engine  1102  derives features from input signals Analog Input Signal  118  and/or Digital Input Signal  116  in the context of attributes of Tool-Specific Feature Functions  1104  (discussed in further detail in Derivation Functions  1200  of FIG. 12). Feature Derivation Engine  1102  is in bilateral data communication with Real-Time Signal Input Engine  1108  in achieving several key functionalities: (1) data reading communication of measurements respective to Analog Input Signal  118  and Digital Input Signal  116 , (2) acquiring data from Reference Data Logic  404 , (3) occasionally acquiring updated Tool-Specific Feature Functions  1104  routines from Human Interface Logic  412 , and (4) data writing communication of derived features and feature values to Real-Time Signal Input Engine  1108  for further communication to Pattern Recognition Logic  406 . Log of Learning Measurements  1106  is in data writing communication with Real-Time Signal Input Engine  1108  for receiving and holding measurements respective to anomalous measured vectors when Real-Time Signal Input Engine  1108  is prompted by Rework Engine  810 . Log of Learning Measurements  1106  also is in bilateral data communication with Human Interface Logic  412  and Network Interface  1116  for further communication or copying of Log of Learning Measurements  1106  data to an operating technician, a floppy, a CD-ROM, or other system. Real-Time Signal Input Engine  1108  is in bilateral data communication with Human Interface Logic  412  for sending classification results and for receiving updated Tool-Specific Feature Functions  1104  routines, for receiving configuration data for hardware signals (for storage in Signal Configuration Schema  1110 ), and for receiving a flag respective to an anomalous measured vector. Real-Time Signal Input Engine  1108  is in bilateral data communication with Feature Derivation Engine  1102  as previously described. Real-Time Signal Input Engine  1108  is in bilateral data communication with Pattern Recognition Logic  406  for sending derived feature values and feature data to Pattern Recognition Logic  406  and for receiving classification feedback respective to feature values and feature data. Real-Time Signal Input Engine  1108  is in bilateral data communication with Reference Data Logic  404  for informing Reference Data Logic  404  of the particular signal being read and responsively acquiring feature data to classify the signal. Real-Time Signal Input Engine  1108  is in bilateral data communication with Real-Time Executive Logic  402  for (a) receiving execution enablement data signals, multi-process and/or multitasking interrupts, and (b) sending feedback and flagging inputs so that responsive logic is executed in a unified and coordinated real-time cadence. Real-Time Signal Input Engine  1108  is in bilateral data communication with Network Interface  1116  for receiving certain measured signal data directly from Network  146  and for interacting with certain external systems via Network  146  as needed. Real-Time Signal Input Engine  1108  is in bilateral data communication with Process Information System Interface  1112  for interfacing with Process Information System  104 ; in Signal Logic Detail  1100  of FIG. 11, Process Information System Interface  1112  is shown using Network Interface  1116  to interface to Process Information System  104 , but the interface can also be via another data communication means such as a direct serial link. PI Buffer  1114  is used for holding data exchanged between Process Information System  104  and Classification Computer System  110  during transfers.  
     [0107]FIG. 12 presents detail in tool-specific feature derivation functions. Derivation Functions  1200  shows further detail in the particular functions used to derive features used in classification of Mechanical Assembly  124 . Each Feature Function contains the logical routine used to derive the features. For any particular signal, as indicated in the discussion of Reference Data Detail  1300  in FIG. 13, a function (Aligned Function  1326 ) and set of attributes (Related Functional Attribute  1328 ) is defined for at least one feature; this data is referenced by Feature Derivation Engine  1102  and which applies the appropriate function in Tool-Specific Feature Functions  1104  to derive the feature values for use in Pattern Recognition Logic  406 .  
     [0108] FFT Feature Function  1202  is generally understood in the art. This function is described in (1) Brigham, E. O., “The Fast Fourier Transform”, Prentice-Hall Inc., 1974 and also in (2) Cooley, J. W. and Tukey, J. W., “An Algorithm for the Machine Calculation of Complex Fourier Series”, Mathematical Computation 19, 1965—which are both incorporated herein by reference.  
     [0109] RPM Feature Function  1204 , Minimum Signal Value Feature Function  1206 , Maximum Signal Value Feature Function  1208 , and RMS Feature Function  1210  are generally understood in the art. These functions are described in  
     [0110] Bannister, R. H., “A review of rolling element bearing monotoring techniques”, Fluid Machinery Committee, Power Industries, London, June 1985;  
     [0111] Callacott, R. A., “Mechanical Fault Diagnosis and condition Monitoring”, Chapman and Hall, London, 1977;  
     [0112] Hunt, T. M, “Condition Monitoring of Mechanical Equipment and Hydraulic plant”, Chapman and Hall, 1996;  
     [0113] Rao, B. K. N., “Handbook of condition monitoring”, Elsevier Advanced Technologies, 1996;  
     [0114] Harris, T. A., “Rolling Element bearing Analysis”, Third Edition, New York, 1991, John Wiley &amp; Sons, Inc.;  
     [0115] Berry, J. E., “How to Track Rolling Element Bearing Health with Vibration Signature Analysis”, Sound and Vibration, 25 (1991) 11, pp. 24-35;  
     [0116] Dyer, D. and Stewart, R. M., “Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis”, Journal of Mechanical Design, Vol. 100, 1978, pp. 229-235.; and  
     [0117] Edgar, G. R. and Gore, D. A., “Techniques for the Early Detection of Rolling Bearing Failures”, SAE Technical Paper Series, 1984, pp. 1-8.  
     [0118] All of these 8 documents are incorporated herein by reference.  
     [0119] Curtosis Feature Function  1212  is generally understood in the art. This function is described in Rush, A. A., “Kurtosis a crystal ball for maintenance engineers”, Iron and Steel International, 52, 1979, S. 23-27, which is incorporated herein by reference. Filtered Curtosis Feature Function  1214  is achieved by time-filtering a Curtosis value.  
     [0120] Envelope Set Feature Function  1216  is generally understood in the art. This function is described in Jones, R. M., “Enveloping for bearing Analysis”, Sound and Vibration, 30 (2) 1996, page 10 which is incorporated herein by reference.  
     [0121] Cepstrum Feature Function  1218  is generally understood in the art. This function is described in Randall, R. B., “Cepstrum Analysis and Gearbox Fault Diagnosis”, Brüel and Kjaer application note No. 233 which is hereby incorporated herein by reference.  
     [0122] CREST Feature Function  1220  is generally understood in the art. This function is described in Bannister, R. H., “A review of rolling element bearing monitoring techniques”, Fluid Machinery Committee, Power Industries, London, June 1985 which is incorporated herein by reference.  
     [0123] Filtered CREST Feature Function  1222  is generally understood in the art. This function is described in (1) Dyer, D. and Stewart, R. M., “Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis”, Journal of Mechanical Design, Vol. 100, 1978, pp. 229-235; and (2) Bannister, R. H., “A review of rolling element bearing monitoring techniques”, Fluid Machinery Committee, Power Industries, London, June 1985. Both of these publications are hereby incorporated herein by reference.  
     [0124] Dimensionless Peak Amplitude Feature Function  1224  is derived from a time signal as a dimensionless parameter. The mean peak height of the time signal characterizes the degree of peak plurality and peak impulse magnitude, and the periodicity and constancy between a peak and two following peaks. To derive the dimensionless parameter of Dimensionless Peak Amplitude Feature Function  1224 , the ratio between the mean amplitude and the signal “base” is first established.  
     [0125] Equation 1  
     [0126] Base level:  
         a   b     =       1   M            ∑     j   =   1     M            abs        (     x   j     )                     with                 M                 samples                 of                 the                 time                 signal                       
 
     [0127] M=Number of data points  
     [0128] x=digital data samples  
     [0129] Equation 2  
     [0130] Average peak amplitude  
         a   MP     =       1   N            ∑     j   =   1     N          a   Pj                       
 
     [0131] N=Number of detected peaks in time signal  
     [0132] a pj =Amplitude of peak j  
     [0133] The Feature of Dimensionless Peak Amplitude Feature Function  1224  is then  
     [0134] Equation 3  
         f   1     =       a   MP       a   b                     
 
     [0135] Dimensionless Peak Separation Feature Function  1226  is derived from a time signal as a dimensionless parameter. An ideal roller bearing damage consistently generates peaks in the time signal from the sensor monitoring the bearing. The constancy of generated peaks (as related to the distances between the peaks) is expressed by calculating all distances between a set of peaks and building the variance to a mean value. A roller bearing in good condition reflects a high degree of variance through small, stochastically distributed signal peaks. To ensure the comparability of different rotation speeds, a dimensionless ratio is established by dividing the variance by the mean distance between peaks.  
     [0136] Equation 4  
     [0137] Average peak distance  
         d   MP     =       1     N   -   1              ∑     j   =   1       N   -   1            d   Pj                       
 
     [0138] N=Number of detected peaks in time signal  
     [0139] d pj =Distance between peak j and peak j- 1   
     [0140] Equation 5  
         σ   P     =       1     N   -   2                ∑     j   =   1       N   -   1              (       d   pj     -     d   MP       )     2                         
 
     [0141] The feature of Dimensionless Peak Separation Feature Function  1226  is then calculated from  
     [0142] Equation 6  
         f   2     =       d   MP       σ   P                     
 
     [0143]FIG. 13 presents a block flow diagram of the reference data logic in the monitoring system. Reference Data Detail  1300  shows detail in Reference Data Logic  404 . Pattern Recognition Logic  406 , Signal I/O Logic  408 , Real-Time Executive Logic  402 , and Human Interface Logic  412  are reprised from FIG. 4. For any particular signal, as indicated in the discussion of FIG. 12, a function (Aligned Function  1326 ) and set of attributes (Related Functional Attribute  1328 ) is defined for at least one feature; this data is referenced by Feature Derivation Engine  1102 , which applies the appropriate function in Tool-Specific Feature Functions  1104  to derive feature values for use in Pattern Recognition Logic  406 . Learning Database  1302  shows a set of records related to a particular Tool ID  1334 . For each Tool ID  1334  there is a set of features, Feature 1 (F1)  1318  through Feature N (Fn)  1320  for which a judgment (from a human expert) is also expressed as a value in Judgment Value  1322  data-field. A set of rows of values showing Feature 1  1318  through Feature N  1320  values and a judgment as a class of operational status is provided for each Tool ID  1334 . In the context of the aligning provided by the design of Candidate Feature Database  1304  and Tools Database  1306  and Component Database  1308 , Learning Database  1302  therefore represents the collected input of human professional understanding (respective to interpretation of the status of Mechanical Assembly  124  in operation) to Classification Computer System  110  so that Classification Computer System  110  provides rapid mechanized access in real-time to that collected understanding. A further discussion of how Feature N  1320  data is assembled is described in the discussion respective to Toolbox Development Overview  2300  in FIG. 25. Further considerations in (1) selecting a proper number of classes (providing an inherent class structure) for articulating judgment and (2) defining acceptable predictability of a classifier instance is discussed in Component Assembly  2200  and Toolbox Development Overview  2300  of FIGS. 24 and 25. Candidate Feature Database  1304  is a table of a set of Features  1324  and a Related Tool Identifier  1330  data-field showing the particular Tool ID  1334  set for which that Feature  1324  is relevant. In this regard, a particular Feature  1324  is any one feature in the set of features (Feature 1  1318  through Feature N  1320 ) in Learning Database  1302  where one Feature N  1320  record is related to one Tool ID  1334 . Aligned Function  1326  logical identifier is also provided along with Related Functional Attribute  1328  so that Feature Derivation Engine  1102  executes the proper function of Tool-Specific Feature Functions  1104  and also determines the appropriate attribute of the derived function in derivation of a particular feature value. Tools Database  1306  is a table of values respective to the variable types Input Channel Logical ID  1332 , Tool ID  1334 , and Tool Identifying Term  1336  (for facilitating human interaction with Reference Data Detail  1300  by providing a lexical string identifier for display on Monitor  102 ). Input Channel Logical ID  1332  is dependent upon a particular Filter Circuit  300  on Band-Pass-Filter Circuitry Board  204 ; the purpose of Input Channel Logical ID  1332  is to enable crosscheck in execution of Hardware Configuration Function  702  so that an operating technician attaches an instance of Analog Input Signal  118  to the proper Signal Wire Terminators  212 . Component Database  1308  provides a further reference so that instances of Component Identifier  1338  (see the further discussion of Component Assembly  2200  in FIG. 24) are, when combined with a particular Sensor Type  1340 , wired to the proper Input Channel Logical ID Field  1342 . Note that, in using Component Database  1308  and Tools Database  1306 , a Component Identifier  1338  in combination with a Sensor Type  1340  “points” to acceptable Input Channel Logical ID Field  1342  values. The Input Channel Logical ID Field  1342  values (which could be more than one relative Signal Wire Terminator  212 ), when mapped to the table of Tools Database  1306 , enable identification of a particular Input Channel Logical ID  1332 ; ID  1332  then identifies an appropriate Tool ID  1334  in alignment with Component Identifier  1338 , Sensor Type  1340 , and Input Channel Logical ID  1332  (resolving hardware alignment considerations in the classifier). Tool ID  1334  then references a set of Feature  1324  instances in Candidate Feature Database  1304  (a datalogical reference for evaluation of Component Identifier  1338  in operation) and also references a particular record of Learning Database  1302  (collected human learning in intersection with the set of Feature  1324  instances in the datalogical reference frame of Candidate Feature Database  1304 ). The set of Features  1324  with their particular Learning Database  1302  instance is then used in conjunction with (a) Progressive Feature Selector  904  (or, alternatively, Evolutionary Feature Selector  902 ) and (b) with Weighted Distance Classifier  906  (or, alternatively, Neural Net Engine  908 ) to derive a subset, for each Judgment Value  1322  class, of (c) Feature 1  1318 -Feature N  1320  features for use in real-time classification. Real-Time Signal Feature Set Instance  1310  is the subset, for each Judgment Value  1322  class, of (c) Feature 1  1318 -Feature N  1320  features for use in real-time classification for a particular Analog Input Signal  118  (Digital Input Signal  116  or Analog Input Signal  118 /Digital Input Signal  116  combination) instance respective to at least one identified judgment class (Judgment Value  1322  type). Real-Time Signal Feature Set Instance  1310  points to a particular Decision Function Set  924  instance and aligns with a respective Decision Feature Set  926 . Real-Time Signal Feature Set Instance  1310  is accessed by Signal I/O Logic  408  in interactions with Feature Derivation Engine  1102  and Pattern Recognition Logic  406 . Feature Data Evaluation Engine  1312  (in data reading communication with Learning Database  1302 , Candidate Feature Database  1304 , Tools Database  1306 , and Component Database  1308 ) is used with Feature Selection Function  714  and Classifier Definition Function  718  in defining a classifier instance. Configuration Tables Interface  1314  is in bilateral data communication with Learning Database  1302 , Candidate Feature Database  1304 , Tools Database  1306 , Component Database  1308 , and Real-Time Signal Feature Set Instance  1310  for loading these tables and providing the operating technician with a full reference frame for evaluating the status of the data which is custom to a particular instance of Mechanical Assembly  124  (note that Configuration Tables Interface  1314  is in bilateral data communication with Human Interface Logic  412  and Real-Time Executive Logic  402 ). Threshold Value  1316  is used by Feature Data Evaluation Engine  1312  in a decision to use Evolutionary Feature Selector  902  in preference to Weighted Distance Classifier  906 . Depending on the capability of the particular Classification Computer CPU  138  and affiliated computing resources, the use of Evolutionary Feature Selector  902  is preferable for feature sets above Threshold Value  1316 .  
     [0144]FIG. 14 presents details for a machine analysis toolbox. Toolbox  1400  shows Machine Analysis Toolbox  1402 . In this regard, in one embodiment, a data schema section is provided with Learning Database  1302 , Candidate Feature Database  1304 , Tools Database  1306 , and Tool-Specific Feature Functions  1104  as an aligned set with a unifying logical identified data value in Data Feature Tool Object  1404 . Machine Analysis Toolbox  1402  is, in one embodiment, unified in one data schema logical section, or, in the embodiment shown in Signal Logic Detail  1100  and Reference Data Detail  1300 , virtually provided in more than one logical section. Attributes A 1  and A 3  shown in column  1328  (FIG. 13) are the feature attributes of the signal vector as derived from feature function  1326  to become classification feature  1324  (as noted earlier, Features frequently reference a variable possessing a joining consideration or datalogical nexus between, first, an attribute derived in the context of a function from the measured signal and, second, a variable used in a classifier). Machine Analysis Toolbox  1402  is, in one embodiment, resident as a logical object set in data form on a unified physical storage device such as a CD-ROM, a “floppy”, or other like media. In this regard, (1) hardware alignment considerations, (2) the datalogical reference for evaluation of components in operation, (3) the related collected human learning in intersection with the datalogical reference frame, and (4) the functions needed to derive the data needed for the datalogical reference frame all continuously improve with time; these elements in the embodiment are beneficially upgraded periodically in Classification Computer System  110  in a unified manner to provide access to improved methodology. Machine Analysis Toolbox  1402 , therefore, is manifested virtually in all embodiments and is manifested in unified logical form in some embodiments and in separated logical form in other embodiments.  
     [0145]FIG. 15 presents an overview flowchart of the organization of key information in constructing and using the preferred embodiments. Use Process Overview  1500  outlines a broad process perspective in use of the classifier. In Setup Step  1502 , a computer-implemented routines set is provided, with each routine deriving a feature value set from a signal generated by a type of sensor when used on a machine component type. In Testing Step  1504 , a set of input signals is collected from each sensor type representative of a machine component in different classified modes (classes) of operation (e.g., without limitation, a Shutdown Class, a Good Class, a Transition Class, and a Bad Class). In Feature Definition Step  1506 , the computer-implemented routines are applied to derive a feature value set for each measured input signal instance, and each feature value set is added to a Learning Database. In Expert Input Step  1508 , a class affiliation parameter value (judgment) is associated with each input signal instance in the Learning Database. In this regard, the “classified modes” of operation of Testing Step  1504  are based on human understanding; in Expert Input Step  1508 , this understanding is datalogically expressed and affiliated with each signal for which a feature value set was derived in Feature Definition Step  1506 . In Toolbox Assembly Step  1510 , the information of Testing Step  1504 , Feature Definition Step  1506 , and Expert Input Step  1508  is organized in the context of the data reference of the routines of Setup Step  1502 . In this regard, the (a) set of sensor identifiers, (b) feature routines related to each sensor type, (c) sets of features defined by the feature routines, (d) learning databases, and (e) affiliated query and configuration routines and data are all collected into a Toolbox Of Data Feature Tools  1402  for use in computer memory. In Use Step  1512 , the Toolbox  1402  is used in configuration and real-time operation of the monitoring system to measure the status of a unified component assembly (Mechanical Assembly  124 ) in operation.  
     [0146]FIG. 16 presents a flowchart of key classification steps. Implementation Process Overview  1600  shows further detail in Use Step  1512 . In Configuration Step  1602 , configuration of Reference Data Logic  404  customizes Classification Computer System  110  to a particular instance of Mechanical Assembly  124  by (a) identifying deployed sensors (see Component Assembly  2200  of FIG. 22); (b) assigning a channel (Signal Wire Terminators  212 ), component/sensor (Component Identifier  1338  &amp; Sensor Type  1340 ), and/or Toolbox Tool ID (Related Tool Identifier  1330 ) to each sensor; and (c) providing historical learning data to Learning Database  1302 .  
     [0147] In Optional Learning Step  1604 , an optional learning phase is implemented to acquire further measurements in the learning base. This is an optional step in the sense that such learning is alternatively acquired in the course of adaptation (Adaptation Step  1610 ); however, in certain applications, it is beneficial to perform system testing prior to full commitment to use so that Learning Database  1302  reflects both (a) measurements and judgments for the type of component and sensor in prior use on other embodiments of Mechanical Assembly  124  or from a test environment and (b) specifically judged measurements for the particular Mechanical Assembly  124  being monitored by the instance of Classification Computer System  110  configured.  
     [0148] In Classifier Derivation Step  1606 , a real-time classifier reference parameter instance (Weighted Distance Real-Time Parameters  916  or NN Real-Time Parameters  914 ) is derived for each component and sensor combination. In Real-Time Classifying Step  1608 , derivation and depiction of real-time membership values (the membership of each component in each class valid for that component) is performed in an ongoing manner. In Adaptation Step  1610 , adaptation of Learning Database  1302  and redefinition of Weighted Distance Real-Time Parameters  916  (or NN Real-Time Parameters  914 ) is executed (via multi-process and/or multitasking interrupts and execution enablement data signals from Executive Logic  402 ) along with on-going derivation and depiction of real-time membership values. In Anomalous Vector ID Step  1612 , anomalous vectors are identified (Rework Engine  810 ). In Human Query Step  1614 , Monitor  102  is queried for operating technician input respective to judgment for the anomalous vector. In Adaptation Decision  1616 , the operating technician inputs a decision to proceed to redefine Weighted Distance Real-Time Parameters  916  (or NN Real-Time Parameters  914 ). If the decision result is NO, Adaptation Decision  1616  terminates to Exit Step  1620 . If the decision result is YES, Adaptation Decision  1616  terminates to Replacement Classifier Derivation Step  1618 . In Replacement Classifier Derivation Step  1618 , a new real-time classifier reference parameter instance is determined via coordination of Adaptation Function  722  in Control Block  604 . Weighted Distance Parameter Instance  916  (or Neural Network Parameter Instance  912 ) provide storage for the redefinition of Weighted Distance Real-Time Parameters  916  (NN Real-Time Parameters  914 ) so that the existing instances of Weighted Distance Real-Time Parameters  916  (NN Real-Time Parameters  914 ) are used for real-time classification of Mechanical Assembly  124  during the adaptation process. In the final portion of Replacement Classifier Derivation Step  1618 , the new version of Weighted Distance Parameter Instance  916  (NN Parameter Instance  912 ) replaces the old version for the particular signal for which the adaptation is being executed. In Exit Step  1620 , the adaptation process concludes with an exit.  
     [0149]FIG. 17 presents a flowchart detailing decisions in use of progressive feature selection, evolutionary feature selection, neural network classification, and weighted distance classification. Classification Overview  1700  further defines Classifier Derivation Step  1606  to show the process by which each measurement vector (derived from Analog Input Signal  118 , Digital Input Signal  116 , or a combination of Digital Input Signal  116  and Analog Input Signal  118  signals) is classified. In Sample Signal Preparation Step  1702 , the signal sample values are normalized for use in classification. This step is not executed in every contemplated embodiment, but is generally a preferable approach. In this regard, “normalized sample signals” reference the normalized features as a whole for a particular set of learning samples taken collectively and resident for a particular Tool ID  1334  in Learning Database  1302 . In Branch Step  1704 , reference rules branch the method to a particular combination of (a) classifier and (b) feature selection process. This branching is further described respective to considerations outlined in Table 8.  
                               TABLE 8                               Evolutionary   Weighted                   Feature   Distance   Progressive       Situation   NN   Selection   Classifier   Feature Selection                  Problems with a   X   X   X   X       small number of       possible input       features (&lt;400)       Problems with a   X   X   X       large number of       possible input       features (&gt;400)       Learning data set   X   X       X       has more then one       disjunct cluster       with equal class       membership       Strong ellipsoidal       X   X   X       distribution for the       data set       High level of           X   X       deterministic       solutions (safety       relevance issues,       minimum of       control parameters)                  
 
     [0150] In PF-WD Preparation Step  1706 , a set of normalized sample signals is prepared for the progressive feature selection process. In PF-WD Class Separation Step  1708 , the normalized sample signal set is separated into class subsets. In PF-WD Feature Set Definition Step  1710 , the weighted distance classifier and the progressive feature selection process converge Learning Database  1302  data for the particular sample signals to a real-time feature subset. In PF-WD Real-Time Set Storage Step  1712 , the real-time feature subset is saved in Weighted Distance Real-Time Parameters  916 .  
     [0151] In PF-NN Preparation Step  1714 , a set of normalized sample signals is prepared for the progressive feature selection process. In PF-NN Class Separation Step  1716 , the normalized sample signal set is separated into class subsets. In PF-NN Feature Set Definition Step  1718 , the neural network classifier and the progressive feature selection process converge Learning Database  1302  data for the particular sample signals to a real-time feature subset. In PF-NN Real-Time Set Storage Step  1720 , the real-time feature subset is saved in NN Real-Time Parameters  914 .  
     [0152] In EF-NN Preparation Step  1722 , a set of normalized sample signals is prepared for the evolutionary feature selection process. In EF-NN Class Separation Step  1724 , the normalized sample signal set is separated into class subsets. In EF-NN Feature Set Definition Step  1726 , the neural network classifier and the evolutionary feature selection process converge Learning Database  1302  data for the particular sample signals to a real-time feature subset. In EF-NN Real-Time Set Storage Step  1728 , the real-time feature subset is saved in NN Real-Time Parameters  914 .  
     [0153] In EF-WD Preparation Step  1730 , a set of normalized sample signals is prepared for the evolutionary feature selection process. In EF-WD Class Separation Step  1732 , the normalized sample signal set is separated into class subsets. In EF-WD Feature Set Definition Step  1734 , the weighted distance classifier and evolutionary feature selection process converge Learning Database  1302  data for the particular sample signals to a real-time feature subset. In EF-WD Real-Time Set Storage Step  1736 , the real-time feature subset is saved in Weighted Distance Real-Time Parameters  916 .  
     [0154]FIG. 18 presents detail in the weighted distance method of classifying and progressive feature selection. Progressive Feature Selection Process  1800  provides an overview of the method executed by Progressive Feature Selector  904 . The set of features Feature 1  1318  to Feature N  1320  for a particular Tool Identifying Term  1336  is processed to define the best subset for use in real-time classification. In this regard, the size of the subset is dependent upon the particular Classification Computer CPU  138  and affiliated resources, the frequency at which real-time membership determinations are desired, the instances of Tool Identifying Term  1336  in Classification Computer System  110 , and like considerations. In Weighted-Distance Classifier Initial Features Step  1802 , the features are individually evaluated if more than 400 features are defined for a particular signal. If less than 400 features are defined, each feature couplet is evaluated. In Weighted-Distance Classifier Initial Feature Ranking Step  1804 , fitness for a classifier respective to each feature or feature couplet is evaluated. In Weighted-Distance Classifier Feature Selecting Step  1806 , the best performing features or feature couplets are selected to Selected Feature Stack  910 . On subsequent iterations, the best feature sets are selected to Selected Feature Stack  910 . In Weighted-Distance Classifier Feature Set Augmentation Step  1808 , the feature sets in the stack are separately augmented with each individual feature not in the set. In Weighted-Distance Classifier Feature Set Fitness Decision  1810 , each new feature set is evaluated for classification prediction fitness. If sufficient fitness prediction is not achieved by any feature set (“NO” decision result), then the process returns to Weighted-Distance Classifier Feature Selecting Step  1806 . If the decision result is YES, Weighted-Distance Classifier Feature Set Fitness Decision  1810  terminates to WD Feature Set Acceptance Step  1812 . In Weighted-Distance Classifier Feature Set Acceptance Step  1812 , the feature set achieving the best fitness is written into Weighted Distance Real-Time Parameters  916  (NN Real-Time Parameters  914 ). FIG. 19 shows further detail in Steps  1804 ,  1806 , and  1808  in feature evaluation detail  2900 . An example of the above process follows.  
     EXAMPLE 2  
     [0155] Control parameters for the selection strategy are similar to Example 1 used to describe Stack  910  in the discussion of FIG. 9. First, in reference (1) to the reclassification rate (predictive capability and/or error) concept and (2) to the basis of a classified learning sample for which an unambiguous class assignment is performed prior to use for each random sample collected during a learning phase, a measure of appraisal is obtained by reclassifying the learning sample with the respective classification algorithm and a selected subset of classifying data. The ratio of (a) the number of random samples correctly classified in accordance with the given class assignment to (b) the total number of random samples investigated provides a measure of the reclassification rate, error, and predictive capability of the particular evaluated classifier and selected classifying data; as should be appreciated, the goal of the process is ultimately to obtain a very small reclassification error. In the ideal case, the decision on class assignment for reclassification agrees with the class subdivision of the learning sample for all objects on the basis of the maximal membership. The advantage of the reclassification error concept is the possibility of determining conclusive values even with a small number of random samples.  
     [0156] Separation sharpness is also a key factor in the example. The classification decision gains unambiguity if the distance between the two largest class memberships increases. Based on these membership values a sharpness factor is defined, which is considered in the selection process if two or more feature combinations have identical classification rates.  
     [0157] Respective to notation, “z” is the Object number for a particular individual having a feature set and membership in a class (i.e. when z is expressed as a numeric value, then F z,x  is considered to have a specific quantitative value in the example; when z is expressed as the textual “z”, then F z,x  is a logically identified variable representing a classifying feature in the example). An Object, therefore, is a feature vector and affiliated class membership value as a combination.  
     [0158] In this example, the feature “gene pool” has a Maximum Set Size of F z,1  . . . F z,10  and the progressive search algorithm determines a sub-optimal feature subset containing 3 features.  
     [0159] Human expert membership value “0” indicates that the sample belongs to class A, and a value “1” indicates that the sample belongs to class B. The human expert&#39;s decision is available for all samples of the learning data base (in this example, a sample size of 20).  
     [0160] In Step  1  of the example, all samples from the learning database are read into the progressive selection method.  
     [0161] In Step  2  of the example, the search algorithm starts with an opening minimum set of 2 features F z,x -F z,y  for each individual (see notational paragraph above respective to variable “z”). All possible combinations of two features are then defined. Table 9 shows all combinations of 2 features containing Feature “1” and the possible feature pairs. The combination F z,1  and F z,2  is defined using the notational form “1|2”.  
                       TABLE 9                          1 | 2   F z,1     F z,2         1 | 3   F z,1     F z,3         1 | 4   F z,1     F z,4         1 | 5   F z,1     F z,5         1 | 6   F z,1     F z,6         1 | 7   F z,1     F z,7         1 | 8   F z,1     F z,8         1 | 9   F z,1     F z,9          1 | 10   F z,1      F z,10                    
 
     [0162] In Table 10 all possible combinations of any two features are listed.  
                       TABLE 10                                      1. 1 | ( 2, 3, 4, 5, 6, 7, 8, 9, 10)           2. .2 | ( 3, 4, 5, 6, 7, 8, 9, 10)           3. 3 | ( 4, 5, 6, 7, 8, 9, 10)           4. 4 | ( 5, 6, 7, 8, 9, 10)           5. 5 | ( 6, 7, 8, 9, 10)           6. 6 | ( 7, 8, 9, 10)           7. 7 | ( 8, 9, 10)           8. 8 | ( 9, 10)           9. 9 | (10)                      
 
     [0163] The performance of each feature combination is determined by (1) training the Weighted Distance Classifier, (2) calculating the classification results for all samples of the learning data set, and (3) comparing the results of the calculation with the initial human expert determination (i.e., establishing the comparison of respective ability of the trained classifier to return, respective to a particular “trial” feature combination, the same determination of membership as the human expert for a particular measurement).  
     [0164] Table 11 demonstrates this process for the feature combination 6|10 after the performance of each feature combination has been determined.  
               TABLE 11                          Classification results for the whole learning data set.                                                     Class                           Member-                           ship                   Membership   Membership   Value                   value for   value for   calculated   Membership               class 1   class 2   from both   Value               predicted   predicted   class   Measured       First   Second   from using   from using   member-   from Human       Feature   Feature   trained   trained   ship   Expert       Value   Value   classifier   classifier   values   Input               F 1,6      F 1,10      0.8   0.2   0   0       F 2,6      F 2,10      0.4   0.6   1   0                           (mis-                           classified)       F 3,6      F 3,10      0.9   0.1   0   0       F 4,6      F 4,10      0.6   0.4   0   0       F 5,6      F 5,10      0.7   0.3   0   0       F 6,6      F 6,10      0.9   0.1   0   0       F 7,6      F 7,10      1.0   0.0   0   0       F 8,6      F 8,10      0.6   0.4   0   0       F 9,6      F 9,10      0.6   0.4   0   0       F 10,6     F 10,10     0.7   0.3   0   0       F 11,6     F 11,10     0.1   0.9   1   1       F 12,6     F 12,10     0.2   0.8   1   1       F 13,6     F 13,10     0.1   0.9   1   1       F 14,6     F 14,10     0.2   0.8   1   1       F 15,6     F 15,10     0.4   0.6   1   1       F 16,6     F 16,10     0.3   0.7   1   1       F 17,6     F 17,10     0.1   0.9   1   1       F 18,6     F 18,10     0.2   0.8   1   1       F 19,6     F 19,10     0.3   0.7   1   1       F 20,6     F 20,10     0.2   0.8   1   1                  
 
     [0165] Two performance indicators are calculated from table 11: (a) the Recall Rate for all samples: Number correct classified/total sample size=19/20=0.95; and (b) the Sharpness as the difference between the class memberships. In the instance that a sample is misclassified, the difference between the membership values is 0. (If more than 2 classes are defined the sharpness is calculated as the difference between the two highest membership values.)  
     [0166] Sharpness=(0.8−0.2)+0.0+(0.9−0.1)+ . . . +(0.7−0.3)+(0.9−0.1)+ . . . +(0.8−0.2)/20.0=0.52  
     [0167] Table 12 gives the result of the evaluation of the combination of features F z,6  and F z,10 .  
                               TABLE 12                                      F z,6     F z,10     95% correct in                   predicting.                      
 
     [0168] Insofar as the objective is (a) to generate a list of the best m feature combinations rather than (b) to store all evaluated feature combinations, a sorted list (Stack  910 ) with a specified stack size is updated after the performance check of the combination regarding Table 10 as previously described.  
     [0169] The stack in Table 13 represents the situation after the evaluation of all combinations inclusive of the feature combination F z,8  and F z,9 . The features are sorted according to (a) the Recall Rate and then (b) for several combinations according to their Sharpness where the Recall Rate is identical.  
               TABLE 13                          Sorted list with a stack size of 10                                     First   Second                   feature   feature   Recall   Sharp-       Pos.   value   value   Rate   ness                                         1   6   10   0.95   0.52       2   6   7   0.95   0.48       3   4   9   0.90   0.45       4   7   10   0.90   0.42       5   6   9   0.85   0.43       6   5   7   0.85   0.40       7   7   8   0.80   0.39       8   4   8   0.80   0.39       9   2   10   0.80   0.37       10   5   9   0.75   0.35                  
 
     [0170] After calculating the performance of the next combination F z,8  and F z,10  (Table 14) the stack is updated if the performance is superior to the performance of the last entry in the stack. In the example the current feature combination F z,8  and F z,10  is ranked at position 5 and the old position 10 falls out of the Stack. (Table 15).  
               TABLE 14                          Current evaluation:                                     First   Second                   feature   feature   Recall   Sharp-           valued   value   Rate   ness                       8   10   0.90   0.42                      
 
     [0171]               TABLE 15                          Updated list after evaluation feature combination 8|10.                                     First   Second                   feature   feature   Recall   Sharp-       Pos.   value   value   Rate   ness                                         1   6   10   0.95   0.52       2   6   7   0.95   0.48       3   4   9   0.90   0.45       4   7   10   0.90   0.42       5   8   10   0.90   0.42       6   6   9   0.85   0.43       7   5   7   0.85   0.40       8   7   8   0.80   0.39       9   4   8   0.80   0.39       10   2   10   0.80   0.37                    
     [0172]               TABLE 16                          Stack after testing all combination with two features.                                     First   Second                   feature   feature   Recall   Sharp-       Pos.   value   value   Rate   ness                                         1   6   10   0.95   0.52       2   6   7   0.95   0.48       3   4   9   0.90   0.43       4   7   10   0.90   0.42       5   8   10   0.90   0.40       6   6   9   0.85   0.43       7   5   7   0.85   0.40       8   9   10   0.80   0.41       9   7   8   0.80   0.39       10   4   8   0.80   0.39                    
     [0173] Proceeding now to Step  3 , all combinations which are stored in table 16 (the best 10 pairs) are successively combined with all features not previously included in this pairing of features. Features for which low measures of quality have been calculated in the appraisal of the feature pairs can thus be re-included in the selection process. Tables 17-19 show phases in Step  3  consideration for three Features.  
               TABLE 17                       All possible combination of the best pair F z,6 ,       F z,10  with all available features.                                                6 | 10 | 1   F z,6 , F z,10 , and F z,1             6 | 10 | 2   F z,6 , F z,10 , and F z,2             6 | 10 | 3   F z,6 , F z,10 , and F z,3             6 | 10 | 4   F z,6 , F z,10 , and F z,4             6 | 10 | 5   F z,6 , F z,10 , and F z,5             6 | 10 | 7   F z,6 , F z,10 , and F z,7             6 | 10 | 8   F z,6 , F z,10 , and F z,8             6 | 10 | 9   F z,6 , F z,10 , and F z,9                        
 
     [0174]               TABLE 18                       Possible combinations of the stack pairs with       all available features.                                                                1.   6   |   10   |   (1, 2, 3, 4, 5, 7, 8, 9)           2.   6   |   7   |   (1, 2, 3, 4, 5, 8, 9)           3.   4   |   9   |   (1, 2, 5, 6, 7, 8, 10)           4.   7   |   10   |   (1, 2, 3, 5, 8, 9)           5.   8   |   10   |   (1, 2, 3, 4, 5, 9)           6.   6   |   9   |   (1, 2, 3, 4, 8, 10)           7.   5   |   7   |   (1, 2, 3, 4, 8, 9, 10)           8.   9   |   10   |   (1, 2, 3, 4, 5)           9.   7   |   8   |   (1, 2, 3, 4, 9, 10)           10.   4   |   8   |   (1, 2, 3, 9, 10)                        
     [0175]               TABLE 19                          Stack after testing all combination with three features.                                                 First   Second   Third                       feature   feature   feature   Recall   Sharp-           Pos.   value   value   value   Rate   ness                                                         1   6   10   5   1.00   0.60           2   6   10   9   1.00   0.58           3   6   10   7   0.95   0.56           4   6   7   3   0.95   0.52           5   6   7   9   0.95   0.50           6   6   10   5   0.90   0.50           7   4   9   5   0.90   0.48           8   6   10   7   0.90   0.47           9   4   9   6   0.85   0.49           10   6   7   8   0.85   0.48                        
     [0176] If the algorithm selects more then three features, the process is repeated (Step  3 ). A criteria is used to either end the process and accept a set of feature combinations or to enhance the feature set to four, five, six, etc. features until an acceptable level of membership prediction is achieved.  
     [0177] Variation of the stack size is a tuning parameter for the system. In this regard, and due to the linear effect of the stack size, the computing time can be shortened considerably by reducing the list length. For example, at a stack size=10, only the 10 best individual features are used in the second stage to form new feature combinations. However, as these are again combined with all N′ features, all features will continue to take part in the selection process, even if they do not belong to the best individual features. As quality in stack performance and the respective stack size tentatively depends considerably on the particular problem instance, a recommendation can, of course, only be given via the selection of the parameter list length (number of solutions to be pursued). However, as a general rule, according to the experience of the inventors, a sensible compromise between optimization of the computing time and the finding of a sub-optimum set of features is achieved with a stack size of preferably between 20 and 50 feature candidate combinations.  
     End of Example 2  
     [0178]FIG. 20 presents detail in the neural network (NN) method of classifying and in evolutionary feature selection. Evolutionary Feature Selection Process  1900  shows a process of use for the evolutionary feature selection process; the classifier used is a neural network, but, in an alternative embodiment, the weighted distance classifier described in Progressive Feature Selection Process  1800  is used along with the evolutionary selection process. In Neural Network Initiation Step  1902 , a particular neural network for use with a sample signal set given a primer configuration and the number of layers and neurons per layer are defined. In Neural Network Initial Fitness Step  1904 , an initial feature set is defined to establish the scope of the network, and fitness of the neural network is evaluated against the initial feature set. In Neural Network Configuration Decision  1906 , the fitness of Neural Network Initial Fitness Step  1904  is examined against a performance threshold to define acceptability of the neural network configuration. If the decision result is NO, Neural Network Configuration Decision  1906  terminates to Neural Network Reconfiguration Step  1908 . If the decision result is YES, Neural Network Configuration Decision  1906  terminates to Primary Random Feature Set Generation Step  1910 . In Neural Network Reconfiguration Step  1908 , if the fitness of Neural Network Configuration Decision  1906  is insufficient, the neural network configuration is examined and modifications are proposed. If the result of Feature Set Size Decision  1926  is YES, the feature set size is decreased and the neural network configuration is examined and modifications are proposed. NN Reconfiguration Step  1908  then terminates to Neural Network Initiation Step  1902  for modification of the neural network configuration. In Primary Random Feature Set Generation Step  1910 , following acceptability of the neural network configuration in Neural Network Configuration Decision  1906 , feature subsets are generated using random feature selection. In Feature Set Ranking Step  1912 , each feature subset is used (a) to train the neural network and derive a weighting matrix and then (b) to use the particular derived weighting matrix parameter instance in Neural Network Parameter Instance  912  to evaluate the sample vectors in predicting their memberships. The feature subsets are then ranked according to their prediction capability. In Feature Set Decision  1914 , each new feature subset is evaluated for classification prediction fitness. If sufficient fitness prediction is not achieved by any feature set, then the process proceeds to Feature Subgroup Selection Step  1918 . If sufficient fitness prediction is achieved by any feature set, then the process proceeds to Neural Network Feature Set Acceptance Step  1916 ; and the feature set defines the (sub-optimal) feature combination for use in NN Real-Time Parameters  914  for the particular signal. In Feature Subgroup Selection Step  1918 , a best-performing subgroup of the ranked feature subsets of Feature Set Ranking Step  1912  are selected for further modification; each of these feature subsets in the subgroup is referred to as a “parent individual”. In Feature Subgroup Crossover Step  1920 , “parent individuals” exchange certain features to define “new individuals”—this process is termed as “crossover”. In Feature Subgroup Mutation Step  1922 , the “new individuals” of Feature Subgroup Crossover Step  1920  are further modified as to features by exchanging a specific number of features which were not included in the initial set of features evaluated in the feature subsets of  1912  with features in the “new individuals”—this process is termed as “mutation”. In Feature Set Reconfiguration Step  1924 , the inferior-performing subgroup of the ranked feature subsets of Feature Set Ranking Step  1912  are replaced with the “new individuals” so that a new set of feature subsets (the “parent individuals” and the “new individuals”) is available. The generation counter is then incremented to designate a new generation of feature subsets for consideration. In Feature Set Size Decision  1926 , change in the feature set size in view of the predictive capability of the prior generation is considered. This decision is determined by operating technician input via Human Interface Logic  412  interfacing or, in an alternative automated embodiment, from interaction with a rule set. If the decision result is NO, Feature Set Size Decision  1926  terminates to Feature Set Ranking Step  1912 . If the decision result is YES, Feature Set Size Decision  1926  terminates to Neural Network Reconfiguration Step  1908 .  
     [0179] An example of the evolutionary selection method according to the preferred embodiments is described in conjunction with reference to FIGS. 21A, 21B,  21 C, and  21 D which show evolutionary method steps and data sets  2800 ; FIGS.  21 A- 21 D also provide diagrams showing affiliations between data variables and data values between dataset instances discussed in Example 3.  
     EXAMPLE 3  
     [0180] In Step  1 , setup of (1) a population size for feature combinations (where each combination is an “individual” in the population), (2) a feature set “gene pool” for the population, and (3) the number of feature “genes” per “individual” is defined. In this example, the feature “gene pool” has a Maximum Set Size of F z,1  . . . F z,10 . An opening Minimum Set of 2 features F z,x -F z,y  for each individual is defined. A set of 5 individuals in the population is defined.  
     [0181] Respective to notation, “z” is the Object number for a particular individual having a feature set and membership in a class (i.e. when z is expressed as a numeric value, then F z,x  is considered to have a specific quantitative value in the example; when z is expressed as the textual “z”, then F z,x  is a logically identified variable representing a classifying feature in the example). An Object, therefore, is a feature vector and affiliated class membership value as a combination.  
     [0182] Proceeding to Step  2 , the 5 individuals (note that the “individuals” of Table 20 are defined at the datalogical level of variables rather than at the level of specific measured Objects) with the selected minimum number of features (the 2 feature “gene combinations” of Step  1 ) are defined as a set of feature variables from the feature “gene pool” of F z,1  . . . F z,10  in a random manner to form Table 20 (further reference to Dataset  2802  of FIG. 21A).  
                       TABLE 20                          F Z,1     F Z,8     Combination 1-forming               Individual 1       F Z,4      F Z,10     Combination 2-forming               Individual 2       F Z,6     F Z,2     Combination 3-forming               Individual 3       F Z,3     F Z,1     Combination 4-forming               Individual 4       F Z,5     F Z,9     Combination 5-forming               Individual 5                  
 
     [0183] In Step  3 , the new feature combinations are used in relating to the Learning Data Set (Samples  2804 ,  2806 ) in Learning Database  1302  so that prior combined measurements of feature values and membership value combinations are acquired for training a classifier. In this first pass, (the Minimum Set of) 2 features F z,x -F z,y  for each individual define a Feature Value Couplet in the Learning Data Set. In this example, essentially the simplest case, 2 measurements (Sample A  2804  and Sample B  2806 ) from the learning database are recovered showing past human evaluations of two measured situations (the evaluations being expressed quantitatively as Human Expert Membership Values) using Features 1-10 respective to a Membership Class A:  
     [0184] F 1,1  . . . F 1,10  having a Human Expert Membership Value 1  
     [0185] F 2,1  . . . F 2,10  having a Human Expert Membership Value 0  
     [0186] Human Expert Membership Value “1” or “0” indicates, respectively, whether or not the particular Feature Value combination measured instance (the Feature Value Couplet of this first pass) belongs to Class A. Two Objects in the database (note again that each F x,y  represents a quantitative value from a feature respective to a sample from the learning database) are read into the evolutionary selection method. Note again that only two feature values of the possible 10 in any one sample Object are used in this first evaluation.  
     [0187] Proceeding to Step  4 , “weight adaptation” is performed to associate (a) data values from learning with (b) the combinations of features identified from random selection. Reviewing Steps  2  and  3 , Table 20 was used to define all relevant feature values; then each relevant class membership is also affiliated with each Feature Value couplet respective to the learning database as shown (see Table 21 and Dataset  2808  of FIG. 21A for the Feature Value Couplets of this first pass with their associated Human Expert Membership Values). A consideration of the connections between Dataset  2802 , Dataset  2808 , and Learning Database  1302  in FIG. 21A shows datalogical nexus in this regard. In performing “weight adaptation” in this first pass, the neural network is trained respective to all of the Feature Value Couplets and their affiliated Membership Values shown in Table 21; or, alternatively, the Weighted Distance Classifier has a set of eigenvalues and eigenvectors defined respective to all the Feature Value Couplets and their affiliated Membership Values shown in Table 21 and Dataset  2808 . The Neural Net, then, is trained according to the values of Table 21; or, alternatively, the Weighted Distance Classifier is trained according to the values of Table 21. The training step is shown in FIG. 21A as Derive Classifier Operation  2810 . Derive Classifier Operation  2810  obtains values from Column  2812 , Column  2814 , and Column  2816  of Dataset  2808  (note that, even as the columns are conveniently identified, the system continues to relate to each Object, or effective row across all columns referenced, as a related data entity for use in classification).  
                       TABLE 21                       First   Second   Membership Value       Feature   Feature   Measured from       Value   Value   Human Expert Input                  F 1,1     F 1,8     1       F 2,1     F 2,8     0       F 1,4      F 1,10     1       F 2,4      F 2,10     0       F 1,6     F 1,2     1       F 2,6     F 2,2     0       F 1,3     F 1,1     1       F 2,3     F 2,1     0       F 1,5     F 1,9     1       F 2,5     F 2,9     0                  
 
     [0188] In Step  5 , either (1) the trained Neural Network or, alternatively, (2) the trained Weighted Distance Classifier is used to generate Predicted Membership Values according to the quantitative Feature Value Couplets of Table 21. This is shown as Derive Predicted Membership Values Operation  2818  in FIG. 21A. In this regard, values from Column  2812  and Column  2814  of Dataset  2808  are read into Operation  2818  along with the Classifier Reference Instance ( 918 ,  912 ) derived in Operation  2810 . Comparison of the Predicted Membership Value defined by the trained NN (trained WDC) to the Human Expert Membership Value originally measured is then performed. This is shown figuratively in Table 22 and in Dataset  2820  of FIG. 21B. Note that Dataset  2820  acquires its values from Column  2812 , Column  2814 , and Column  2816  of Dataset  2808  and also from Operation  2818  (note again that, even as the columns are conveniently identified, the system continues to relate to each Object, or effective row across all columns referenced, as a related data entity for use in classification).  
                           TABLE 22                               Membership Value                   Predicted from using                   trained classifier                   (note these are examples of   Membership Value       First   Second   what the newly-trained   Measured from Human       Feature   Feature   classifier defines as a   Expert Input       Value   Value   Membership Value set)   (Table 21 value)                  F 1,1     F 1,8     1   1       F 2,1     F 2,8     1   0       F 1,4      F 1,10     0   1       F 2,4      F 2,10     1   0       F 1,6     F 1,2     1   1       F 2,6     F 2,2     0   0       F 1,3     F 1,1     1   1       F 2,3     F 2,1     0   0       F 1,5     F 1,9     0   1       F 2,5     F 2,9     1   0                  
 
     [0189] From examination of Table 22 and Dataset  2820 , conclusions (shown in Table 23) about the classification usefulness of individuals of Table 20 are drawn respective to the proposed plan of randomly-defined Table 20; these conclusions are based upon the performance (in this first pass) of the Feature Value Couplets and affiliated Membership Values recovered as Objects from the Learning Database according to the defined individuals of Table 20 when used by the classifier deployed.  
                       TABLE 23                          F Z,1     F Z,8     50% correct in predicting since, as shown in Table 22,               one sample was properly classified and one sample was               not properly classified       F Z,4      F Z,10     0% correct in predicting since, as shown in Table 22,               both samples were improperly classified       F Z,6     F Z,2     100% correct in predicting since, as shown in Table 22,               each sample was properly classified       F Z,3     F Z,1     100% correct in predicting since, as shown in Table 22,               each sample was properly classified       F Z,5     F Z,9     0% correct in predicting since, as shown in Table 22,               both samples were improperly classified                  
 
     [0190] In Step  6 , the five individuals of Table 20 are ranked according to their performance in predictive classification. Table 23 now is rearranged into Table 24. Dataset  2822  of FIG. 21B also shows the data arrangement of Table 24. In tracing the data-linkages shown between Dataset  2820  and Dataset  2822 , the specific considerations of the conclusive (rightmost column) column of Table 24 and Dataset  2822  respective to the data in Table 22 (Dataset  2820 ) are demonstrated. Note that Table 23 is not shown as a dataset in the Figures.  
                       TABLE 24                          F Z,6     F Z,2     100% correct in predicting since, as shown in Table 22,               each sample was properly classified       F Z,3     F Z,1     100% correct in predicting since, as shown in Table 22,               each sample was properly classified       F Z,1     F Z,8     50% correct in predicting since, as shown in Table 22,               one sample was properly classified and one sample was               not properly classified       F Z,5     F Z,9     0% correct in predicting since, as shown in Table 22,               both samples were improperly classified       F Z,4     F Z,10     0% correct in predicting since, as shown in Table 22,               both samples were improperly classified                  
 
     [0191] Proceeding now to Step  7 , two of the combinations (individuals) of Table 20 are selected for generation of “children” in a set of two operations termed “crossover” and “mutation”; in this regard, and in the context of the definition of new “children”, the two chosen individuals of Table 20 are referenced as “parents”. The process is further shown in FIG. 21C. FIG. 21C reprises Dataset  2802 . In example, the F z,6 -F z,2  combination is randomly chosen and the F z,5 -F z,9  combination is also randomly chosen (note, in spite of the fact that an “individual” may have been a “poor performer” in the prediction evaluation, the “individual” is still valid as a “parent” for creating a “child” for the system). Dataset  2826  shows the  2  parent features sets in FIG. 21C and the random choosing action is denoted as Operation  2824 . In the crossover process itself (Step  8  and also indicated as Crossover  2828  in FIG. 21C) the F z,5 -F z,9  and the F z,6 -F z,2  features are exchanged. In crossing over, a feature “gene” from each of two randomly selected “parents” in Table 20 is used as one of each of the child feature “genes” (an examination of the datalinkages between Datasets  2830  and  2832  as they influence Datasets  2834  and  2836  further clarifies the crossover operation). The Table 20 “generation” has now become the Table 25 “generation” insofar as two “children” have been added to the original population of individuals of Table 20.  
                       TABLE 25                          F Z,1     F Z,8     Individual 1       F Z,4     F Z,10     Individual 2       F Z,5     F Z,2     Individual 3 - a child of Table 20 parents F z,5  - F z,9  and               F z,6  - F z,2         F Z,3     F Z,1     Individual 4       F z,5     F z,9     Individual 5 (a parent)       F z,6     F z,2     Individual 6 (a parent)       F Z,6     F Z,9     Individual 7 - a child of Table 20 parents F z,5  - F z,9 and                 F z,6  - F z,2                    
 
     [0192] In Step  9 , mutation of the new children of the Table 25 generation is performed (see Mutation Operations  2846  in FIG. 21C). In this regard, one of Features F z,1  to F z,10  which is not one of the feature “genes” of the new children in the generation of Table 24 is randomly selected for use in substitution (in each child) for a feature gene directly inherited from one of the parents in Operations  2838  and  2840 . Operations  2842  and  2844  then execute to randomly discard one gene from each Child (Datasets  2834  and  2836 , with the discarded feature “genes” shown as Blanks  2856  and  2858  of respective Datasets  2848  and  2850 ). The Features selected for substitution are then substituted the discarded feature “genes” (Blanks  2856  and  2858 ) in the children of Table 25. In example, Individual 7 is mutated to replace F z,6  with F z,7  and Individual 3 is mutated to replace F z,2  with F z,4  (see the movements from Dataset  2848  and  2850  into Datasets  2852  and  2854  with the inclusion of the features selected in Operations  2838  and  2840 ). The Table 25 “generation” has now mutated into the Table 26 (Dataset  2856 ) “generation”. The combination of Datasets  2802 ,  2852 , and  2854  into Dataset  2856  is diagrammed in FIG. 21D.  
                       TABLE 26                          F Z,1     F Z,8     Individual 1       F Z,4     F Z,10     Individual 2       F Z,5     F Z,4     Individual 3 - a now mutated child of Table 20 parents F z,5  -               F z,9 and  F z,6  - F z,2         F Z,3     F Z,1     Individual 4       F z,5     F z,9     Individual 5 (a parent)       F z,6     F z,2     Individual 6 (a parent)       F Z,7     F Z,9     Individual 7 - a now mutated child of Table 20 parents F z,5  -               F z,9 and  F z,6  - F z,2                    
 
     [0193] In Step  10 , which can be termed “survival of the most fit”, the two worst-performing individuals of Table 20 (Fz z,4 -F z,10  &amp; F z,5 -F z,9 ) are replaced by the two new mutated children of Table 26 in Operation  2858 ; put another way, since only 5 combinations (individuals) are permitted in the performing population of a particular “generation”, a new Table for evaluation is defined from the three best performing “old folks” of Table 20 and the 2 new “mutated children” (who are too “young and untested” to be designated as either good or bad performers yet, but who are presumed to have predictive potential until tested otherwise) of Table 26. The process is further appreciated from the diagram of FIG. 21D which shows Dataset  2856  modified by Operation  2858  to remove individuals F z,4 -F z,10  &amp; F z,5 -F z,9  according to the inputs of reprised Dataset  2822 . The removal of individuals F z,4 -F z,10  &amp; F z,5 -F z,9  is shown with respective Remove  2860  and Remove  2862  designators. The other individuals of Database  2822  are retained according to designator Retain  2864 . The new Table for evaluation is shown as Table 27 and as Dataset  2866 :  
                               TABLE 27                                      F Z,1     F Z,8     Combination 1           F Z,5     F Z,4     Combination 2           F Z,6     F Z,2     Combination 3           F Z,3     F Z,1     Combination 4           F Z,7     F Z,9     Combination 5                      
 
     [0194] Table 27 is then substituted for Table 20 and the process is repeated by returning to either Step  1  or Step  2 . A criteria (not shown but which should be apparent in the context of the discussion) is used to (1) end the process of generation definition and evaluation and (2) accept a set of feature combinations; in the absence of achieving satisfaction of the criteria after a sufficient number of returns to Step  2 , the feature “gene set” is enhanced (Step  1  is revisited from Step  8  to enhance the “gene set” per individual) to three (four, five, six, etc.) features, and the generation definition and evaluation process continues until an acceptable level of membership prediction (fulfillment of the criteria) is achieved.  
     End of Example 3  
     [0195]FIG. 22 presents an overview of interactive methods and data schema in the preferred embodiments for use of the weighted distance classification method and a progressive feature selection methodology. Progressive Selection with Weighted-Distance Characterization  2000  and Evolutionary Selection with Neural-Network Characterization  2100  (FIG. 23) overview informational and data design considerations for key broad data schema, functions, and parameter types in interaction with the methodologies used in the preferred embodiments. In this regard, a number of designations by the user are appropriate in crafting application of the embodiments to classification of a particular Mechanical Assembly  124 . Progressive Selection with Weighted-Distance Characterization  2000  depicts an overview of the process which converges to a real-time feature subset by use of the Weighted Distance Classifier and Progressive Feature Selection method (Progressive Feature Selection Process  1800 ). Evolutionary Selection with Neural-Network Characterization  2100  depicts an overview of the process which converges to a real-time feature subset by use of the Neural Network and Evolutionary Selection method (Evolutionary Feature Selection Process  1900 ). As noted in Classification Overview  1700 , alternative plans of use for the Progressive Feature Selection method (Progressive Feature Selection Process  1800 ) with the Neural Network or, alternatively, the Evolutionary Selection method (Evolutionary Feature Selection Process  1900 ) with the Weighted Distance Classifier are also contemplated; however, configuration decisions of these should be apparent in the context of the discussion of Progressive Selection with Weighted-Distance Characterization  2000  and Evolutionary Selection with Neural-Network Characterization  2100 .  
     [0196] Plan 1 Approach  2002  requires Learning Database  2008  data and defined criteria for acceptable performance in Target Function  2012 ; an initial number of features, stack size, and fitness limit criteria are also defined by the user prior to configuration for System Parameters  2014 . In this regard, the nature of the instance of Mechanical Assembly  124  to be monitored and controlled, the confidence needed to remove Mechanical Assembly  124  from operation for maintenance, and the capital at risk in Mechanical Assembly  124  should all be considered in setting performance criteria.  
     [0197] These same considerations are needed in Plan 2 Approach  2102  (FIG. 23) of Evolutionary Selection with Neural-Network Characterization  2100  (respective to Learning Database  2108 , Target Function  2112 , and System Parameters  2114 —with the parameter types of System Parameters  2114  also including population size and operators respective to evolutionary selection operations).  
     [0198] Progressive Selection  2004  (FIG. 22) shows the endpoint of Plan 1 Approach  2002 , the execution of feature definition from Feature Set  2006  and System Parameters  2014  using Fitness Function  2016  as generated from Weighted Distance Classifier  2018  in the context of Target Function  2012  and Class Structure  2010 . Fitness Function  2016  is essentially defined by Weighted Distance Classifier  2018  once Target Function  2012  and Class Structure  2010  are provided.  
     [0199]FIG. 23 presents an overview of interactive methods and data schema in the preferred embodiments for use of the neural network classification method and an evolutionary feature selection methodology. Evolutionary Selection  2104  shows the endpoint of Plan 2 Approach  2102 , the execution of feature definition from Feature Set  2106  and System Parameters  2114  using Fitness Function  2116  as generated from Neural Network Classifier  2118  in the context of Target Function  2112  and Class Structure  2110 . Fitness Function  2116  is essentially defined by Neural Network Classifier  2118  once Target Function  2112  and Class Structure  2110  are provided.  
     [0200]FIG. 24 presents a unified mechanical assembly of machine components and attached sensors. Component Assembly  2200  shows an exemplary instance of Mechanical Assembly  124  to show detail in interactions between components of Mechanical Assembly  124 , sensors, and Signal Filtering Board  114 . Motor  2202  has components Left Motor Bearing  2208  and Right Motor Bearing  2210 . Gearbox  2204  has components Left Gearbox Bearing  2212  and Right Gearbox Bearing  2214 . Centrifuge  2206  has components Left Centrifuge Bearing  2216  and Right Centrifuge Bearing  2218 . Left Motor Bearing  2208  is monitored by Sensor  2220  with the combination being designated in Component Database  1308  as a first instance of Component Identifier  1338  and Sensor Type  1340 ; Right Motor Bearing  2210  is monitored by Sensor  2222  with the combination being designated in Component Database  1308  as a second instance of Component Identifier  1338  and Sensor Type  1340 ; Left Gearbox Bearing  2212  is monitored by Sensor  2224  with the combination being designated in Component Database  1308  as a third instance of Component Identifier  1338  and Sensor Type  1340 ; Right Gearbox Bearing  2214  is monitored by Sensor  2226  with the combination being designated in Component Database  1308  as a fourth instance of Component Identifier  1338  and Sensor Type  1340 ; Left Centrifuge Bearing  2216  is monitored by Sensor  2228  with the combination being designated in Component Database  1308  as a fifth instance of Component Identifier  1338  and Sensor Type  1340 ; and Right Centrifuge Bearing  2218  is monitored by Sensor  2230  with the combination being designated in Component Database  1308  as a sixth instance of Component Identifier  1338  and Sensor Type  1340 . Sensor  2220  generates a time-variant electrical voltage signal to Signal Wire Terminators  212   a.  Sensor  2222  generates a time-variant electrical voltage signal to Signal Wire Terminator  212   b.  Sensor  2224  generates a time-variant electrical voltage signal to Signal Wire Terminator  212   c.  Sensor  2226  generates a time-variant electrical voltage signal to Signal Wire Terminator  212   d.  Sensor  2228  generates a time-variant electrical voltage signal to Signal Wire Terminator  212   e  (per Band-Pass-Filter Circuitry Board  204 , a second instance of Signal Filtering Board  114  in Classification Computer System  110  is provided for this channel and the channel respective to Sensor  2230 ). Sensor  2230  generates a time-variant electrical voltage signal to Signal Wire Terminator  212   f.  Connector  2232  connects Right Motor Bearing  2210  and Left Gearbox Bearing  2212  to provide either a rigorous or essentially rigorous coupling. Connector  2234  connects Right Gearbox Bearing  2214  and Left Centrifuge Bearing  2216  to provide either a rigorous or essentially rigorous coupling.  
     [0201] With regard to sensors used in gas turbine monitoring, U.S. Pat. No. 5,612,497 for an “Adaptor For Mounting A Pressure Sensor To A Gas Turbine Housing”, which issued on Mar. 18, 1997 to Hilger Walter, Herwart Hönen, and Heinz Gallus, is useful in acquiring a signal from compressor air pressure fluctuations; this patent is hereby incorporated by reference.  
     [0202]FIG. 25 presents a block flow summary showing toolbox development information flow for a particular set of unified mechanical assemblies and machine components. Toolbox Development Overview  2300  depicts sources from which data values for Machine Analysis Toolbox  1402  are acquired. Plant Experience  2302  shows experience gained over time from operation of a particular instance of Mechanical Assembly  124 . Test Bench Information  2304  represents data gained from test bench work from operation of particular components in simulated test situations. Historical Data  2306  represents (1) the historical assembly of experience from operation of various instances of Mechanical Assembly  124  and (2) data values from respective Candidate Feature Database  1304  and Learning Database  1302  instances. Data acquired from the literature augments Plant Experience  2302  and Test Bench Information  2304 . Plant Experience  2302 , Test Bench Information  2304 , and Historical Data  2306  are combined into data for Candidate Feature Database  1304  and Learning Database  1302  information when configuring an instance of either Weighted Distance Real-Time Parameters  916  or NN Real-Time Parameters  914 .  
     [0203]FIG. 26 presents a view of key logical components, connections, and information flows in use of the monitoring system in a monitoring use of the preferred embodiment. Concurrent Monitoring Processes  2400  shows key processes which are essentially simultaneously active and interactive in providing functionality in monitoring and (optionally) adaptive controlling in use of the embodiments. Signal Transmitting Operation  2402  represents the process of sensing motional attributes of components in Mechanical Assembly  124  and conveying an electrical signal in real-time to a Signal Wire Terminator  212  instance. Data Preprocessing Operation  2404  shows actions responsive to the electrical signal in Signal Filtering Board  114  to generate a Signal Filtering Board  114  output signal. A/D Operation  2406  shows actions responsive to the Signal Filtering Board  114  output signal in Data Acquisition Board  112 . Digital Data Processing Operation  2408  shows further linearization actions in Real-Time Signal Input Engine  1108  on the Data Acquisition Board  112  output digital value to provide a signal for Feature Derivation Engine  1102  processing. Collected Classifying Logical Operations  2410  summarizes logical operations executed by Classification Computer Logic  140 . Classifying Operation  2412  summarizes operations using Signal I/O Logic  408 , Pattern Recognition Logic  406 , Reference Data Logic  404 , and Human Interface Logic  412 . Displaying Operation  2414  summarizes operations using Human Interface Logic  412  to output information to an operating technician. Networking Operation  2416  summarizes operations using PI Buffer  1114  and Network Interface  1116 . Real-time coordination Operation  2418  shows needed support processes such as a Windows or DOS Operating System (Windows and DOS are trademarks of Microsoft Corporation) and operations of Real-Time Executive Logic  402 . Storage Operation  2420  shows the storage of data either within Classification Computer Logic  140  or in an external system such as Process Information System  104  or a system accessed via Network  146 . Process Controlling Operation  2422  shows actions in Process Information System  104 , Communications Interface  106 , and Control Computer  108 .  
     [0204]FIG. 27 presents a view of key logical components, connections, and information flows in use of the monitoring system in an adaptive control use of the preferred embodiment. Adaptive Controlling Processes  2500  further expands on the depiction of processes of Concurrent Monitoring Processes  2400  to show further details in some processes, key infological processes, and data sources. Classifying Operation  2412  has further detail shown in the actions of Classifier Adaptation Operation  2502 , Machine Analysis Toolbox  1402 , Classification Operation  2506 , Feature Selection Operation  2508 , Candidate Feature Generation Operation  2510 , Judgment Input Operation  2516  (provided by a configuration expert), and Database Management Operation  2518  (also provided by a configuration expert). Details of Band-Pass-Filter Circuitry Board  204  are further shown in the processes of Apparatus Functional Operation  2526 , Process Control Sensing Operation  2524 , Direct Sensing Operation  2528 , Real-time Control Operation  2522 , Judgment Input Operation  2516 , Process Signal Reading Operation  2514 , and Process Data Reading Operation  2512 . Displaying Operation  2414  details are further depicted as processes shown in Display Operation  2504  and Results Communication Operation  2520 . Results Communication Operation  2520 , Real-time Control Operation  2522 , and Command Signal Operation  2530  also show the processes which “close the loop” to enable adaptive control of Mechanical Assembly  124  according to the results of Classification Computer Logic  140  analysis. In the context of Adaptive Controlling Processes  2500  and its depiction of co-existent operations, Apparatus Functional Operation  2526  shows operational Mechanical Assembly  124 .  
     [0205]FIG. 28 shows an example of a graphical icon depiction of class affiliation parameter values in normalized form, and FIG. 29 shows an example of a graphical icon depiction of class affiliation parameter values in non-normalized form. Normalized Membership Depiction  2600  shows output on Monitor  102  for communication of classification of Mechanical Assembly  124  to an operating technician. “Good” Normalized Membership Value  2602  shows the membership of Mechanical Assembly  124  in operation in a “Good” Class. “Transitional” Normalized Membership Value  2604  shows the membership of Mechanical Assembly  124  in a “Transitional” Class. “Bad” Normalized Membership Value  2606  shows the membership of Mechanical Assembly  124  in a “Bad” or “Unacceptable” Class. The overall status of Mechanical Assembly  124  according to Normalized Membership Depiction  2600  communicates a need for awareness and vigilance on the part of the operating technician. Normalized Membership Depiction  2600  shows normalized values—i.e., the total of “Good” Normalized Membership Value  2602 , “Transitional” Normalized Membership Value  2604 , and “Bad” Normalized Membership Value  2606  are forced to equal 100% (as a second normalization after lo normalization of input data according to Sample Signal Preparation Step  1702 ). Basic Membership Depiction  2700  of FIG. 29 shows an example of non-normalized or basic data. “Good” Basic Membership Value  2702  shows the membership of Mechanical Assembly  124  in a “Good” Class, “Transitional” Basic Membership Value  2704  shows the membership of Mechanical Assembly  124  in a “Transitional” Class, and “Bad” Basic Membership Value  2706  shows the membership of Mechanical Assembly  124  in a “Bad” Class; but, in Basic Membership Depiction  2700 , the sum of “Good” Basic Membership Value  2702 , “Transitional” Basic Membership Value  2704 , and “Bad” Basic Membership Value  2706  is not 100%. Both Normalized Membership Depiction  2600  and Basic Membership Depiction  2700  output characterizations to an operating technician are valid in use of the preferred embodiments, depending on the preferences of the operating technician and configuring expert.  
     [0206] The approach of the Strackeljan dissertation, the toolbox,  30  and the adaptive capability of the described embodiments provide a new system for machine diagnosis which enables an integrated solution to machine monitoring and adaptive control while also providing for rapid deployment of a diagnostic system respective to the installation date of a new machine.  
     [0207] The described embodiments are achieved within a number of computer system architectural alternatives. In one embodiment, an IBM Personal Computer 300PL using a 400 MHz CPU with a 6 GB Hard Drive from IBM Corporation and a Windows 98 operating system by Microsoft Corporation provides a platform for Classification Computer System  110 . Other operating systems such as Microsoft&#39;s earlier DOS operating system can also be used. In one alternative, an embodiment is facilitated within the context of a multi-process environment wherein the different databases, data sections, and logical engines are simultaneously installed and activated with data transfer linkages facilitated either directly or indirectly via the use of a data common and/or an application program interface (APIs). In another alternative, the different databases, data sections, and logical engines are facilitated within the context of a single process environment wherein different components are sequentially activated by an operating technician with linkages facilitated either directly or indirectly via the use of data commons or data schema dedicated to interim storage. In yet another alternative, the different databases, data sections, and logical engines are deployed within the context of a single process environment wherein (a) some components of the different databases, data sections, and logical engines are accessed and activated by an operating technician with linkages facilitated either directly or indirectly via the use of data commons or data schema dedicated to interim storage, and (b) the other components within the different databases, data sections, and logical engines are accessed by calls with previously-installed routines. In one alternative, the classifier, different databases, data sections, and logical engines are implemented and executed on one physical computer. In another alternative, the different databases, data sections, and logical engines are facilitated on different platforms where the results generated by one engine are transferred by an operating technician to a second or other plurality of the different databases, data sections, and logical engines executing on different computer platforms, although a separate operating system is needed on each platform. In yet another alternative, the classifier, different databases, data sections, and logical engines are facilitated on a plurality of computer platforms interconnected by a computer network, although a separate operating system is needed on each platform and the operating system further incorporates any networking logic that is needed to facilitate necessary communications via such a computer implemented communication network. Many of the different gradations of architectural deployment within the context of the above overview are considered by the applicants to be generally apparent, and the illustration of the present invention can be conveniently modified by those of skill, given the benefit of this disclosure, to achieve the utility of the present invention within the context of the above computer system architectural alternatives without departing from the spirit of the present invention once given the benefit of the disclosure.