Abstract:
A distributed stress wave analysis system is disclosed for detecting structure borne sounds cause by friction. The detected information is processed using feature extraction and neural network artificial intelligence software. The system consists of stress wave sensors, interconnect cables, and preferably three modules: (1) distributed processing units, (2) maintenance advisory panel, and (3) laptop computer. A derived stress wave pulse train which is independent of background levels of vibration and audible noise is used to extract signature features, which when processed by neural networks of polynomial equations, characterize the mechanical health of the monitored components. The system includes an adjustable data fusion architecture to optimize indication thresholds, maximize fault detection probability, and minimize false alarms.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The invention generally relates to mechanical diagnostic systems, and particularly to a distributed stress wave analysis system for detecting structure borne sounds caused by friction. 
     2. Description of the Prior Art 
     All diagnostic techniques are selected to detect discrepant components and monitor their rate of degradation up to the end of their useful life. Thus, they are closely related to inherent reliability and to the rate at which components degrade from fault initiation to loss of intended function. 
     Since diagnostic techniques are often used during scheduled inspections, the aircraft maintenance and inspection concept forms a critical interface with diagnostics. Diagnostics is the “gate” through which basic reliability, failure progression intervals, and the maintenance/inspection concept must interface to produce the availability, safety, and component removal rates for the overall system. 
     One of the key relationships in the detection of failures is the one between the failure progression interval and the inspection interval. This relationship determines how often the diagnostic technique will have a chance to detect a discrepancy during the progression of a failure. The concept of this relationship, is what has caused frequent inspections on army aircraft, for example, in order to provide maximum probability of detection. 
     Army helicopters diagnostic techniques are typically optimized to assure minimum accident rate. However, this has been accomplished at the cost of mission reliability (unnecessary aborts) and mean time between removals (“MTBR”) (due to incorrect or premature removals). Over the past several decades, many millions of dollars have been spent to improve the inherent reliability and failure progression intervals related to historically significant failure modes. However, in many cases, the major MTBR and mission reliability benefits of a higher mean time between failures (“MTBF”) and slower failure progression cannot be realized without some improvement to the diagnostics gate. 
     There are four basic parameters that define the accuracy and effectiveness of any diagnostic technique. These parameters are defined as follows: 
     P 1 —the probability of calling a good part good; 
     P 2 —the probability of calling a good part bad (this parameter represents false indications and P 2 =1−P 1 ); 
     P 3  —the probability of calling a bad part good (these are undetected levels of degradation P 3 =1−P 4 ); and 
     P 4 —the probability of calling a bad part bad (these are correctly identified degraded parts). 
     The effectiveness of any diagnostic technique depends upon the detection threshold employed to indicate a degraded condition. For each possible detection threshold, there is an associated set of P 1  and P 4  effectiveness values. Variations of the detection threshold invariably produce divergent changes in P 1  and P 4 . 
     The effectiveness parameters P 1  and P 4  are also related to the number of times that a diagnostic technique is used during the progression of a fault from “initial discrepancy” to “end of useful life”. The more often the test is made, the better the chance of detecting the failure process. While the “instantaneous” probability of detecting a fault is a function of the detection threshold, the “cumulative” probability is a function of the technique utilization interval and the rate of failure progression. The following equation defines how instantaneous probabilities convert to cumulative probabilities of fault detection. 
     
       
         P 4c =1−(1−P 4d )  Equation A 
       
     
     Where: 
     P 4c =The cumulative probability of detection after “n” uses of the diagnostic technique. 
     P 4d =The probability of detection after each use of the diagnostic technique (a “decision cycle”). 
     n=The number of times the diagnostic technique is used during the failure progression interval. 
     The MTBR and diagnostic technique cost effectiveness are inseparable elements in setting the indication thresholds for any current or proposed technique. Except for the cost savings attributable to accidents prevented by diagnostic/prognostic indications, there is no other area where significant cost savings can be achieved in environments such as the army aviation environment. The MTBR is an expression of the rate at which all component removals occur, regardless of whether or not the removals were justified. Thus, incorrect removals due to false diagnostic indications are a contributing factor to the overall MTBR. Accordingly, it is vital to set indication thresholds that will (a) reliably detect the presence of degradation early in the failure progression interval, and (b) have a very low probability of false indication when used to test healthy components for a period of time that is greater than the component&#39;s inherent MTBF. This same type of analysis also applies to the mean time between precautionary landings, mission aborts, and maintenance actions for diagnostic techniques and indications that result in these events. 
     It is to the effective resolution to achieve these accurate indications that the present invention is directed. 
     SUMMARY OF THE INVENTION 
     The present invention provides a distributed stress wave analysis system for detecting structure borne sounds cause by friction. The detected information is processed using feature extraction and polynomial network artificial intelligence software. The system consists of stress wave sensors, interconnect cables, and preferably three modules: (1) distributed processing units (“DPU”), (2) maintenance advisory panel (“MAP”), and (3) laptop computer (“LTC”). 
     Where the system is applied to helicopter drive train components, the sensors, DPU and MAP can be airborne components, while the LTC can be ground based. The DPU can have a serial interface for integration into an airborne Flight Data Recorder or Health Usage Monitoring System (“HUMS”). 
     The stress wave analysis (“SWAN”) portion of the system is a high frequency acoustic sensing and signal conditioning technology that provides a time history of friction and shock events in a machine, such as a helicopter drive train. The SWAN portion of the system is similar to the stress wave analysis described and shown in U.S. Pat. No. 5,852,793, issued to Board et al. (the &#39;793 Patent), the disclosure of which is incorporated herein by reference. A derived stress wave pulse train (“SWPT”) is independent of background levels of vibration and audible noise. The SWPT preferably is digitized and used to extract signature features, which when processed by neural networks of polynomial equations, characterize the mechanical health of the monitored components. 
     The system includes an adjustable data fusion architecture to optimize indication thresholds, maximize fault detection probability, and minimize false alarms. System testing preferably indicates a 100% probability of detecting gear or bearing damage within one hour of operation with a discrepant condition, and less than a one tenth of one percent chance of a false alarm during 1000 hours of operation with healthy components. In addition, to accurately detecting faults, the software used by the system will locate a fault, isolate its cause to either a gear or bearing source, display the percent degradation, and estimate its remaining useful life. 
     The application of artificial intelligence techniques for classification of SWPT features advances current technology to achieve accurate, real time, diagnostic capability at all flight power levels. However, it should be recognized that the hardware and operating system software of the present invention are readily adaptable to numerous other mobile and fixed based applications, and all applications are considered within the scope of the invention. 
     Accordingly, it is an object of the invention to provide a system that performs distributed stress wave analysis on one or more components of a machine or equipment. 
     It is another object of the invention to provide a system for reliably detecting the presence of degradation of a component early in the failure progression interval. 
     It is another object of the invention to provide a system that has a very low probability of false indication when used to test healthy components for a period of time that is greater than the component&#39;s inherent mean time between failure. 
     It is still another object of the invention to provide a system that estimates the remaining useful life of a degraded component. 
     It is yet another object of the invention to minimize downtime of equipment and machines. 
     In accordance with these and other objects which will become apparent hereinafter, the instant invention will now be described with particular reference to the accompanying drawings. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a block diagram of the Distributed Stress Wave Analysis system of the present invention; 
     FIG. 2 is a block diagram of the Diagnostic &amp; Prognostic Data Fusion Architecture employed to implement Data Fusion for the Distributed Stress Wave Analysis system of Figure 1; 
     FIG. 3 is a block diagram of certain programs implemented by the laptop computer illustrated in FIG. 1; 
     FIG. 4 illustrates the principal user interface screen displayed by the laptop computer illustrated in FIG. 1; 
     FIG. 5 illustrates a portion of a Stress Wave Pulse Train Time History file; and 
     FIG. 6 illustrates a user “Evaluation Form” for use with the present invention. 
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     As seen in FIG. 1 the overall system of the present invention is illustrated and generally designated distributed stress wave analysis system (“DSWAN”)  20 . System  20  generally consists of one or more, and preferably a plurality of, stress wave sensors  30 , interconnect cables and three types of modules: (distributed processing units (“DPU”)  50 , a maintenance advisory panel (“MAP”)  70 , and a laptop computer (“LTC”)  96 , such as, though not limited to, a conventional laptop computer having a PENTIUM or higher processor and preferably running WINDOWS 95 or higher. However, it should be understood that system  20  is not limited to used with PENTIUM processors and WINDOWS operating software, and other microprocessors and operating software can be used with the present invention and all are considered within the scope of the present invention. Sensor cables are used to connect DPU  50  to sensors  30 , whereas serial communications and power cables connect DPU  50 s to MAP  70 . A MAP input cable can also be provided for routing equipment power (i.e. 28V DC aircraft power) to MAP  70  for distribution to DPUs  50 . 
     Preferably, sensors  30 , DPUs  50  and MAP  70  are airborne components of system  20  and LTC  96  is ground based, when system  20  is used in conjunction with helicopter drive train components. However, system  20  can be used in many other mobile and fixed based applications, including, but not limited to, diagnosing the condition of gears and bearings in numerous types of mechanical drive trains such as radar components, shipboard machinery and main propulsion equipment, and automotive and helicopter transmissions. Individual DPUs  50  can also be directly interfaced with other mobile and/or fixed based computers through a suitable communications bus. 
     Preferably, each DPU  50  scans up to eight (8) sensor  30  locations, extracts the friction and shock signal from broadband noise, and uses Anomaly Detection Network (“ADN”)  52  software to detect abnormal friction/shock signatures from the monitored components. The eighth sensor  30  can have a dual function of (1) monitoring an associated component and (2) being used by the self-testing software to test and calibrate the associated DPU  50 . Each DPU  50  also contains Diagnostic and Prognostic Network (“DPN”) software for Sensor Validation Networks (“SVN”)  54 , Regime Recognition Networks (“RRN”)  56  and a self-test. When a potential problem is detected by a DPU  50 , an associated indicator is turned “ON” on MAP  70 . LTC  96  can be used, during a post flight inspection, to download, analyze, and display, detected and forecasted problems. LTC  96  can also be used to upload new or additional software to the memory of DPU  50 , or to reprogram DPU  50  for use with a different set of sensors  30 . 
     In one embodiment, each DPU  50  preferably consists of four (4) circuit card assemblies (“CCA”) and two (2) flexible printed circuits (“FPC”) contained within an enclosure having two (2) external input/output (I/O) connectors preferably mounted on one side. The four CCAs preferably consist of a Digital Signal Processing (“DSP”) module, a Serial Communications and Peripheral Device Control (“SIO”) module, a Sensor Interface and Signal Conditioning (“SIF”) module, and a Power Supply (“PS”) module. 
     The DSP module is used as DPU  50 &#39;s system controller and processes the digitized sensor  30  signals. The DSP module, through its central processing unit (“CPU”), communicates with the peripheral resources of DPU  50 . 
     The SIO module can be used as (1) a DPU  50 &#39;s internal resource communications interface control, (2) a DPU  50 &#39;s external serial communications interface control, (3) a fault detection discrete external interface. The SIO module can also be used for (1) analog to digital conversion, (2) digital to analog conversion, (3) battery backed-up memory, (4) power distribution and (5) temperature monitoring. The SIO module preferably contains the peripheral interface control logic, which enables the CPU to communicate with all of the resources within DPU  50 . These resources include (1) control and status registers, (2) serial communications controller (“SCC”) (which supports both synchronous and asynchronous protocols), (3) temperature sensors, (4) battery backed SRAM, (5) analog to digital converter (“ADC”) which converts the conditioned sensor  30  input signal to a digital code for processing by the CPU, (6) digital to analog converter (“DAC”) for converting digital code to an analog signal and is used to generate a waveform to test the signal conditioning electronics and the path of each sensor  30  input, and (7) input and output discretes. 
     Power supply voltages are routed to the ADC to enable power supply monitoring. The SRAM is preferably capable of retaining its data at supply voltages of two volts and with very little current. The SRAM preferably is switched to battery power when main power is not applied and provides nonvolatile storage for stress wave energy data in the absence of power from system  20 . 
     Optically isolated discretes provide an external interface that can be used to set and reset a fault indicator on MAP  70 . A re-programmable device can be provided for controlling the logic functions located on the SIO module. The SIO module distributes power to the DSP module and to the SIF module, preferably through mating connectors. 
     The SIF module is used for sensor constant current drive and multiplexing interface, sensor input signal conditioning, signal conditioning electronics calibration, analog to digital converter input signal multiplexing, emulator interface, programmable integrated circuit programming interface, and temperature monitoring and also includes digital potentiometers. The SIF module provides constant current drive, input buffering, and input multiplexing circuitry to support a plurality of sensor channels, such as eight channels. The SIF module also provides analog signal filtering circuitry for separating stress wave signals detected by sensors  30  from broadband background noise and vibration. 
     The analog signal filtering circuitry also demodulates the high frequency stress wave signal to a low frequency stress wave pulse train. The stress wave pulse train is then digitized for analysis by the DPN software. The SIF module also provides input multiplexing to the ADC, as well as the de-multiplexing used to steer the DAC output (for testing purposes) to each of sensor  30  inputs. 
     The SIF module provides a digital temperature sensor, analog torque signal input, and analog discrete input having a programmable trigger threshold, and an input for allowing recorded stress wave pulse train data to be injected into DPU  50  for analysis. SIF module can also provide attachment points and signal buffering for the emulator of the system and the interface used for programming the re-programmable device on the SIO module. 
     In one embodiment, constant current is generated by sensor  30  input circuitry provided by a current regulator diode forward biased by preferably twenty-four volts DC. Sensor  30  inputs can be buffered, multiplexed to a single output, high pass filtered to eliminate DC offset, and then fed through signal conditioning electronics. The signal conditioning electronic section of the SIF module can include digitally controlled programmable gain stages, a digitally controlled clock tunable bandpass stage, a demodulation stage and a digitally controlled clock tunable lowpass filter stage. The output of the SIF electronic section, and possible other points of the section, are preferably fed to the input multiplexer of the ADC. A test signal, obtained from the DAC, can be injected into the sensor  30 -input buffer to test, as well as calibrate, the circuitry. 
     The PS module can be used for input power conversion for DPU  50 &#39;s electronics usage, real time clock, battery (i.e. rechargeable NiCad) for battery backed-up memory, integrated battery charger, and temperature monitoring. In the absence of power, the battery keeps the real time clock running and retains data in the battery backed-up memory located on the SIO module. The PS module can include a plurality of DC to DC power converters. Preferably, the real time clock is provided with an integrated trickle battery charger. 
     MAP  70  can preferably be a communications panel for interfacing the DPUs  50  of system  20 , via conventional serial RS-232 communication lines. MAP  70  can fuse and distribute voltage to DPU(s)  50 . MAP  70  preferably has one indicator for each DPU  50 , which gives a visual indication of detected faults. In one embodiment, MAP  70  accepts power from the monitored equipment (i.e. aircraft) and distributes it to the individual DPU  50 s. MAP  70  can have a plurality of indicators, such as four, each under the control of a different DPU  50 . The indicators can be magnetically latched and maintain their state until instructed to reset. The indicators preferably provide a visible alert of the detection of an anomalous condition by a DPU  50 . 
     The software used by DPU  50  serves three basic operations: (1) perform self-test (a) power on—comprehensive self-test can be performed, and (b) normal operations—confidence self-test which can run periodically; (2) serial communications routines—communicate to LTC  96  and debug ports, and allow a user to transfer data, reconfigure DPU  50 , and control the operation of DPU  50 ; and (3) sensor  30  tests—contains all necessary functions to process sensor  30  inputs and determine the health of the monitored components. 
     DPU  50 &#39;s software preferably consists of an infinite loop, where sensor  30  testing is initiated on boot-up or by command. The software evaluates sensor  30  inputs to determine if the equipment (i.e. aircraft) is operational. The equipment can be considered operational, where a predetermined amount of sensors  30 &#39;s stress wave energy (“SWE”) values are above a minimum value preferably corresponding to a minimum load upon which the diagnostic algorithms have been trained. Once such condition is met, the feature and transient capture data can be save in DPU  50 &#39;s non-volatile buffers. 
     Preferably the sensor  30 &#39;s data is saved during every scan cycle, until at least a certain number of scan cycles (i.e. 10) is saved within the buffer. Once the required number of scan cycles have been saved, the sensor feature data can be saved at predetermined minimum intervals (i.e. every twenty seconds). 
     The digitized time history (transient capture) data is also saved when the processed time domain feature data from any individual sensor&#39;s ADN has been determined to be a worst case event. In such event, corresponding data from all other sensors  30  is also saved. Once the buffer is full, the oldest cycle record will be replaced with the newly acquired data. 
     Preferably, the operating mode of the monitored equipment or machine is considered “In Regime” when conditions are within specified torque range. Multi-Sensor Regime Recognition  56  software is used, based on predetermined amount of sensors  30  indicating operation within the range, to indicate that the overall system torque is considered to be within the operating regime. When “In Regime” sensors  30  are checked preferably each time they are scanned using one of SVNs  54 . Where a sensor  30  fails to indicate valid a predetermined amount of times (i.e. 8 of 10), it is then considered “Invalid”. The sensor  30  can regain a “Valid” status, where its outcome returns to “normal” as determined by the SVN  54 . However, where sensor  30  is deemed “Invalid”, any Fault Detection Network (“FDN”)  80   a  that requires its input, will preferably be disabled from processing. 
     Each sensor  30  has its own network and confidence test  58  to determine if an anomaly has occurred. If any ADN  52  determines an anomaly condition, at least one of MAP  70 &#39;s indicators is turned on. DPU  50  continues to collect sensor and transient capture data, until a time that the SWE levels fall below the minimum operational limits, or are otherwise commanded to halt or stop. Downloading of the sensor history data and transient capture data from DPU  50 &#39;s memory is preferably commanded by LTC  96 . 
     The software used by LTC  96  allows a user to collect data from DPUs  50  and store the collected data in a database for analysis. The SWAN software allows the user to run more detailed analysis of an event, plot trends in the collected data, and create output for reports. LTC  96  serves the following four functions: (1) connect to DPU  50 s via MAP  70  serial interface; (2) download data from DPU  50 ; (3) evaluate certain conditions based on downloaded data; and (4) re-configure DPU  50  by uploading new code and/or arguments. As illustrated in FIG. 3, LTC  96  contains a main program (LTC.exe) and a DSP I/O program (pserialio.exe). The Main Program provides the user interface while the DSP I/O program handles the interface with the DSP module. FIG. 4 illustrates the principal user interface screen, which is displayed by LTC  96 . 
     LTC  96 &#39;s menu preferably consists of the following items: 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 File 
                 Used to exit the program 
               
               
                 Admin 
                   
               
               
                   Delete Test Cases 
                 Used to remove collected data 
               
               
                   Users 
                 Used to update login information 
               
               
                 Set 
                   
               
               
                   Identification 
                 Used to identity the test data set 
               
               
                   I/O Timeout 
                 Used to set the timeout limit for 
               
               
                   
                 LTC/DPU communications 
               
               
                 DPU 
                   
               
               
                   Data 
                 Used to download DPU data 
               
               
                   Start/Stop 
                 Used to start/stop the DPU 
               
               
                   Reset 
                 Used to reset the DPU 
               
               
                   Self-test 
                 Used to initiate DPU self-test 
               
               
                   Software 
                 Used to upload new software to the DPU 
               
               
                   Time 
                 Used to set the DPU clock 
               
               
                 UUT 
                   
               
               
                   Evaluate 
                 Used to evaluate UUT health, using saved data 
               
               
                   Graphs 
                 Used to plot features of saved data 
               
               
                 Window 
                   
               
               
                   Tile 
                 Standard windows feature 
               
               
                   Cascade 
                 Standard windows feature 
               
               
                 Help 
                 Used to view version information 
               
               
                   
               
             
          
         
       
     
     The DPU-Data menu item stops DPU  50  and fetches the DPU Part, DPU Unit Under Test (“UUT”), DPU Status, Elapsed Operating Minutes (“EOM”), ID Prom, Op code, In Regime and Out of Regime data for each sensor  30  that indicates an anomaly failure and, all DPU  50  test records. This data is placed in the database. 
     The UUT-Evaluate menu item recovers data from the database and evaluates the UUT health as follows: (1) Use Fault Location Network and X of n Confidence Test to validate any indicated sensor anomalies; (2) Choose the validated sensor anomaly with the most extreme data; (3) Use Fault Location networks to evaluate for Port Input Module (“PIM”), Starboard Input Module (“SIM”) and/or Main Module (“MM”) fault; (4) Use Fault Isolation networks to evaluate Gear and/or Bearing fault; (5) Compute Percent degradation; and (6) Compute Useful life. 
     Feature Extraction (“FE”) software is preferably provided for accurately characterizing the SWPT and compressing Time History (“TH”) files  74  created from the digitized SWPT data. The FE software can be modular, and can also be used in two versions: (1) Analyst Mode and (2) DPU mode. The Analyst Mode is preferably used in a PC environment for developing input tables for the synthesis and test of the DPNs. The DPU mode preferably provides the operational form of the software, as required by DPU  50  and LTC  96 . Preferably, two types of FE are employed (1) Time Domain (“TD”)  70  and Frequency Domain (“FD”)  72 . TD  70  begins with TH file  74  of the SWPT. Mathematical transforms are preferably applied to the time series data for characterization of various waveform features including pulse amplitude, duration and energy content. FD  72  analyzes the waveform preferably using Fourier transform techniques and provides, through a spectrum analysis subroutine, an output of signal amplitude as a function of frequency. 
     Thus, FE  70  extracts TD features from SWPT data files. The TDFE  70  software preferably compresses the data into the various waveform features. As seen in FIG. 5, a portion of a SWPT TH file  74  is illustrated. Window (“W”) is preferably a user-defined number of milliseconds (typically selected as the period corresponding to a characteristic machine frequency). W&#39;s length is preferably constant for the full data record. A data record length (“R”) is the total time duration represented by the data file. 
     Preferably, all but two of the features extracted from SWPT  74  depend upon exceeding a limit threshold “L”. This limit L is calculated for each window preferably as a multiple of the mean of the lowest 10% of positive values of the instantaneous amplitude “A i ” of the SWPT during the window. The Limit Threshold Factor (LTF) for computing L is constant for the full record length and can be set by the analyst. The LTF preferably affects thirty-five (35) out of thirty-seven (37) time domain features and is used to calculate a threshold that determines when the SWPT signal is “peaking” v. at a background level. A default value for LTF in DPU  50 &#39;s TDFE  70  software can be provided, such as the value five (5). 
     The following represents definitions for items appearing in FIG.  5  and features calculated from SWPT time history file  74 : 
     (a) Stress Wave Peak Duration (SWPD)—The period of time between an upward breech of the threshold L and when the A i  next falls below L; 
     (b) Stress Wave Peak Amplitude (SWPA)—The maximum value of A i , during the SWPD; 
     (c) Stress Wave Peak Energy (SWPE)—The sum of (A i −L) for each data point during the SWPD; 
     (d) Stress Wave Peak Energy per Window (SWPE/W)—The sum of all the individual SWPE values within a window; 
     (e) Stress Wave Energy per Window (SWE/W)—The numeric sum of all the A i  values greater than zero for all data points that occur during all windows of a data record; 
     (f) Peak Energy Factor per Window (PEF/W)—The ratio of the SWPE/W to the SWE/W; 
     (g) Stress Wave Energy per Record (SWE/R)—The numeric sum of all the A i  values greater than zero for all data points that occur during all windows of a data record; 
     (h) Stress Wave Peak Energy per Record (SWPE/R)—The sum of all the individual SWPE values within a record; 
     (i) Peak Energy Factor per Record (PEF/R)—The ratio of the SWPE/R to the SWE/R; 
     (j) Peaks per Record (PEAKS/R)—The total number of SWPT peaks that occur during a record; and 
     (k) Stress Wave Peak Amplitude per Record (SWPA/R)—The maximum A i  value during the record. 
     Four Statistical Parameters (S 1 , S 2 , S 3  and S 4 ) can also be calculated for each of the 5 Window Length features, for the full record. These same 4 statistical parameters (describing Probability Density Distributions of the extracted features) are calculated for all the individual SWPA values in the record. This preferably yields 37 time domain statistical parameters of the SWPT. Statistical Parameters (S 1 , S 2 , S 3  and S 4 ) are defined as follows: 
     (1) S 1 —3rd moment test for Normal Distribution; (2) S 2 —Maximum value of the population; (3) S 3 —The ratio of (Maximum−Mean)/(Maximum−Minimum); and (4) S 4 —Ratio of the standard deviation of the population to the mean of the population. 
     The Analyst Mode version of the FE software preferably formats the 37 time domain features into a spreadsheet that accurately characterizes the SWPT in the Time Domain. The DPU Mode of the FE software, stores the extracted features, preferably as a block of 32 bit integer words, suitable for processing by the DPN&#39;s stored in DPU  50 &#39;s memory, and for download to LTC  96 &#39;s Ground Station. 
     Frequency Domain Feature Extraction (“FDFE”)  72  begins with SWPT TH file  74  being processed by an FFT spectrum analysis module. Stress Wave Spectral Density (“SWSD”) spectra are obtained by RMS averaging, preferably, ten (10) individual time records with 60% overlap of data. Each SWSD can be converted to a table, which identifies the signal amplitude within a predetermined amount of frequency lines. The provided spectral range contains all of the fundamental defect frequencies for the monitored equipment. The frequency lines are then grouped into bands (i.e. 65 bands of 20 lines—50 Hz), and the average amplitude for all 1300 lines, and the maximum amplitude in each band, are preferably calculated. The ratio of the maximum in each band to the 1300 line average is calculated and tabulated as “peak to average” ratios for each band. FDFE  72 &#39;s software next computes the ratio of the maximum amplitude in all 1300 lines to the average of all 1300 lines. Lastly, the standard deviation can be calculated for the “peak to average” ratios for the bands. The “peak to average” ratios, the 1300 line amplitude magnitude, the max to average ratio, and the standard deviation of the “peak to average” ration are preferably tabulated as sixty eight features that characterize the SWPT in frequency domain. 
     System  20  preferably includes six different decision making networks: (1) Anomaly Detection Networks (“ADN”)  52 ; (2) Fault Detection Networks (“FDN”)  80   a;  (3) Fault Location Networks (“FLN”)  80   b;  (4) Fault Isolation Networks (“FIN”)  82 ; (5) Sensor Validation Networks (“SVN”)  54 ; and Regime Recognition Networks (“RRN”)  56 . These networks preferably consist of polynomial equations, which are implemented as separate software objects. 
     Features, which accurately characterize the SWPT in both the time and frequency domains, are computed with custom Feature Extraction Software. These computed features are then used as inputs to decision making networks of polynomial equations that detect discrepant conditions and measure their severity. Networks of polynomial equations are used to automatically classify the stress wave data as being representative of either healthy or discrepant mechanical components. Numeric modeling software is used to synthesize and evaluate Diagnostic Networks, which use a set of “features” extracted from the SWPT as inputs. 
     As seen in FIG. 2, the architecture of how the different diagnostic and prognostic networks relate to each other in system  20  is illustrated. The starting point for each of the networks is a digitized time history file  74  of the SWPT signal that is the output of DPU  50 &#39;s analog signal conditioner (“ASC”). File  74  is processed by Time Domain Feature Extraction (“TDFE”) software module  70  to compress the data. In one embodiment, file  74  represents approximately 80 k bytes (based on a 2 second 20 KHz capture), which is compressed by TDFE module 70 to approximately 2.64 K bytes of information. However, it should be understood that the amounts of information that can be contained by file  74  and other ratios of compression can be performed by TDFE module  70 , and all are considered within the scope of the invention. 
     The ASC and operating system of DPU  50  monitor corresponding sensors  30  for opens and shorts. If a sensor Open or Short is detected, processing is discontinued for algorithms that require input from the faulty sensor  30 . Each sensor  30  can be provided with a SVN  54  which is a diagnostic network that detects greater than a certain value, such as 10%, loss of sensitivity by a sensor  30 . Where SVN  54  consistently indicates that the sensitivity has dropped by more than the preselected value, system  20  will discontinue processing algorithms that require input from the faulty sensor  30  and a “Sensor “N” Out of Cal” message can be logged for post flight download to LTC  96 . The consistency of failure indications from SVN  54  is measured by “X of N Confidence Test”  58 . 
     RRN  56  is programmed to classify or categorize stored data preferably according to whether or not the main rotor torque (or other main equipment Load or Speed Parameter) is within a specific range of values (i.e. 25,000&lt;TQMR&gt;35,000 for main rotor torque). This information is required to assure SVN  54 , % Degradation  84 , and Remaining Useful Life  86  estimates are based upon a relatively narrower range of reference operating conditions. The broader this reference range, the less accuracy for sensor validation and prognostic indications. RRN  56  uses time domain features from multiple sensors mounted on the monitored equipment. RRN  56  requires persistent indication of operation within the predetermined load range (by applying an X of N confidence test  57 ) to eliminate transient/transition conditions from being labeled as “In Regime” (“IR”). Only IR labeled data is used for sensor validation  54 , trended data plots  88 , and prognostic computations of percent degradation  84  and remaining useful life  86 . 
     SVNs  54  are provided to detect the occurrence of a sensor/cable assembly that has degraded by loss of sensitivity. A separate SVN  54  is required for each sensor  30 . Each sensor  30 &#39;s SVN  54  is a multi-sensor network using multiple features from each sensor  30  as inputs. To train SVNs  54  to recognize a low output sensor  30 , a Sensor Signal Simulator (“SSS”) can be used to generate a SWPT at preferably 90% of “normal” gain for the discrepant sensor location. This low sensitivity signal is then processed by the Time Domain Feature Extraction  70  software to compute baseline features. System  20  can also include OPEN and SHORT detection for sensors  30 . Where a sensor OPEN/SHORT condition is detected, DPU  50 &#39;s software turns on MAP  70  indicator for download and display by LTC  96 , and all networks requiring the particular sensor  30 &#39;s input are disabled, until sensor  30  is returned to normal status. 
     An ADN  52  is preferably provided for each sensor mounted on the main rotor transmission assembly, or other main component of a piece of equipment or machinery being monitored or analyzed. ADN  52  detects the presence of abnormal friction and shock events in the SWPT time domain signal. ADN  52  provides the first step in detecting and confirming the existence of a discrepant condition. Through X of N confidence test  53 , an ADN  52  that consistently indicates the presence of abnormal features in SWPT will trigger an indicator on MAP  70  that indicates a requirement for post flight data download to LTC  96 . Thus, when any single sensor  30 &#39;s ADN  52 &#39;s output consistently indicates a high probability of abnormal friction and shock events, MAP  70  indicator for that particular DPU  50  trips to indicate the need for further evaluation. 
     ADN  52  can be trained or programmed based on previous extracted data. In one embodiment, ADN  52  is trained to indicate an output of “0” when the input data is from a “good” (i.e. baseline) assembly, and indicates a “1” when the input data is obtained from a “bad” (i.e. seeded fault case) assembly. ADN  52  can also be trained by incorporating time domain feature sets that are computed by DPU  50  from simulated sensor  30  data. 
     A Sensor Signal Simulator (“SSS”) can perform an inverse transform of demodulated SWPT recorded data (i.e. tape recorded data), to convert it to an amplitude modulated signal (i.e. 38KHz) which represents “raw” unconditioned output from a stress wave sensor  30 . The raw signal is conditioned (amplified, band pass filtered, and demodulated) by DPU  50 , which also computes and stores its time domain features. The time domain features preferably include variations due to different “time slices” for each two-second record. The simulated data also contains variability due to analog conditioning circuits within DPU  50 . Preferably, these variables are incorporated into the training of ADN  52  to improve their “robustness”. 
     Where data is downloaded as a result of an ADN  52  alarm, all time domain features from the memory of DPU  50  are transferred to a database located within LTC  96 . The features include data from the last “N” measurements on each of sensors  30  connected to corresponding DPU  50 , as well as data acquired at predetermined intervals, such as five minutes, for a predetermined period of time, such as fourteen hours of operational monitoring. “N” represents the number of measurements to complete “X of N” confidence test  81  for FDN  80   a.  The value of “N” is not required to be the same for tests  58 ,  57 ,  53  and  81  associated with SVN  54 , RRN  56 , ADN  52  and FDN  80   a,  respectively. 
     Individual sensor ADN  52  alone cannot provide a fault alarm. Rather, ADN  52  alarms trigger a requirement for a DPU  50  download to LTC  96  where the multi-sensor FDN  80   a  algorithm performs additional analysis to confirm or reject the indication received from ADN  52 . 
     FDN  80   a  uses the time domain features from multiple sensors  30  to confirm the existence of a fault. FDN  80   a  runs only after at least one ADN  52  has detected anomalous readings from one or more of sensors  30  monitoring an assembly. When the algorithm of FDN  80   a  indicates the presence of a discrepant condition, for at least X of N measurement cycles, based upon time domain features from all relevant sensors  30  scanned during a measurement cycle, a fault is considered confirmed and an alarm is displayed on LTC  96 . 
     The selected decision cycle (d) is the amount of time required to complete the measurement and process data for making a pass/fail decision of X of N confidence test  53  that is applied to successive outputs of each sensor  30 &#39;s ADN  52 . The probability of calling a bad part bad at the end of a decision cycle (P 4 d) is a function of the P 4  associated with each measurement (“P4m”), and with the values of X and N for ADN confidence test  53 . The value of P 4 m is in turn a function of the ADN Decision Threshold which is determined by analysis of a “False Alarm, False Dismissal by Threshold” (“FAD”) report associated with ADN  52  for each sensor  30 . 
     The FAD report can list the number of correct alarms, correct dismissals, false alarms, and false dismissals, preferably in a database, of examples used to test the accuracy of ADN  52 . Thus, with this information, P 4 m, the probability that an individual ADN  52  processed measurement will call a bad part bad, is calculated as:              P4m   =       Correct                 Alarms         Correct                 Alarms     +     False                 Dismissals                 Equation                 B                                
     Similarly, P 2 m (the probability that an individual measurement will call a good part bad is calculated as:              P2m   =       False                 Alarms         Correct                 Dismissals     +     False                 Alarms                 Equation                 C                                
     The calculations of P 4 m and P 2 m are a function of the evaluation threshold found in the FAD report. This limit is preferably set to minimize the number of false alarms and the number of false dismissals. As these two types of false indications are inversely related, some judgment should be used in selecting an optimum alarm limit (“Decision Threshold”). In making such judgment, the effect of X of N test  53  on P 4 d and P 2 d (the probabilities of a valid or false alarm from a decision cycle) is considered, as well as the number of decision cycles during the failure progression interval (“Pi”). 
     The failure progression interval can be defined as the number of operating hours between the time when the fault is detectable (with an accuracy of P 4 m or better) and the time when further operation is undesirable (due to considerations such as safety, availability, mission capability, economics and/or logistics). 
     To pass X of N test  53 , at least X of N identically performed measurements must exceed ADN  52 &#39;s Decision Threshold. A binomial distribution can be used to develop the probability of getting a given type of indication upon completion of a decision cycle. Where the probability of each measurement giving a specific type of indication is represented as “Pm” (calling bad part bad—P 4 m or calling a good part bad—P 2 m), then the probability of getting that type of indication at the end of a decision cycle is:                P   d     =       ∑     i   =   x     N              N   !         X   !            (     N   -   X     )     !              (       P   m        x     )            (     1   -     P   m       )       (     N   -   X     )                   Equation                 D                                
     This quantity is known as the cumulative term of binomial distribution. Using equations A through D, the cumulative probabilities of false and valid alarms, for different amounts of operating time can be computed and calculated. 
     Thus, the preferred methodology for optimizing the overall accuracy of system  20  includes the following steps: (1) selecting an indication threshold for individual diagnostic networks (ADNs  52  and FDN  80   a ) to maximize the ratio of correct indications to false indications, and (2) keep the per measurement probability of false indication low enough to preclude a burdensome frequency of downloading data to LTC  96  for analysis by multi-sensor FDN  80   a.    
     The decision threshold for FDN  80   a  should be set to yield a 100% probability of finding a bad part during a predetermined amount of time of operational monitoring, such as ten hours, where such the predetermined amount of time is assumed to be less that the failure progression interval. This threshold is considered the minimum P 4 m requirement for FDN  80   a.    
     The maximum allowable P 2 m requirement for FDN  80   a  is driven by what is assumed to be an acceptable probability of having an unjustified component removal or repair, induced by a false alarm, during an assumed period of defect free operation. With certain assumptions, the minimum P 4 m requirement and the maximum allowable P 2 m requirement for ADNs  52  can also be calculated. 
     Thus, FDN  80   a  analyzes time domain features from multiple sensor locations on an assembly, makes a decision on whether or not there is sufficient evidence to indicate a fault, and implements further analysis if a fault is indicated. Additional analysis routines that can be triggered as a result of a fault indication from FDN  80   a  include: (1) fault location networks  80   b;  (2) fault isolation networks  82 ; and (3) calculation of % degradation  84  and remaining useful life  86 . 
     The time domain feature data downloaded from DPU  50 &#39;s database consist of the inputs to FDN  80   a.  This data includes anomalous data sets that resulted in a Decision Threshold exceedance from one or more of sensor  30 &#39;s ADNs  52 , as well as historical data from all sensors  30 . The data provides input required for both X of N confidence testing, and for historical trending that is used for remaining useful life projections  86 . 
     Preferably, three FLNs  80   b  are provided, for a three (3) module assembly, to locate the module that contains component(s) responsible for a Fault Indication/Alarm. Each FLN  80   b  is trained to locate a fault within one of the three modules of the assembly (where more than three modules are provided, a corresponding number of FLNs  80   b  will also preferably be provided. FLN  80   b  use inputs from multiple sensor  30  locations, and are exercised only after test  81  of FDN  80   a  indicates the existence of a problem. For the example of using system  20  with a helicopter drive train assembly (“helicopter example”), a specific FLN  80   b  is associated with each of the main rotor transmission assembly modules: (1) Port Input Module (“PIM”), (2) Starboard Input Module (“SIM”) and (3) Main Module. A fault condition may or may not result in indications from more than one module. 
     Each FLN  80   b  uses time domain features from multiple sensor  30  locations as inputs. As FLN  80   b  are implemented only after FDN  80   a  has indicated a fault, they are advisory in nature and cannot, by themselves, cause a maintenance action. Thus, the rationale for setting the decision threshold is to achieve a minimum of 90% probability of correctly locating a fault, while minimizing the probability of a false alarm. 
     FINs  82  are also exercised only after test  81  of FDN  80   a  indicates the existence of a problem. Preferably, for the helicopter example, two FINs  82  are provided for each of sensor  30  locations on the main rotor transmission assembly. One FIN  82  is programmed to recognize bearing defects and the other FIN  82  is programmed to identify gear problems. This is done by training FINs  82  to categorize abnormally high amplitude spectral lines as characteristic of either gear or bearing problems. FINs  82  use frequency domain features  72  as inputs and works on the basis of looking for high amplitude spectral lines related to gear and bearing defects. In stress wave analysis, bearing defect frequencies are typically higher than gear defect frequencies. Frequency Domain Feature Extraction  72  is performed by software programmed within LTC  96 , using SWPT time history file  74  as an input. Only when required by a DPU  50  ADN  52  alarm, is a time history file  74  for a particular sensor  30  downloaded to LTC  96  for use as the input to Frequency Domain Feature Extraction  72 . If more than one sensor  30  ADN  52  is in alarm status, then the file  74  that is downloaded to LTC  96  is chosen by saving the time history only for sensor  30  that has the largest exceedance above its ADN  52  alarm threshold. 
     All inputs to FINs  82  are from FDFE  72  software. Preferably, both FDFE  72  and FIN  82  software are contained within LTC/ground station software. Preferably, two FINs  82  are provided for each sensor  30 , one for the gears and one for the bearings. This allows FINs  82  to identify the presence of multiple causes for a detected defect condition (i.e. when secondary damage occurs) rather than having to choose only one failure mode. As stated above, FIN networks  82  run only after FDN  80   a  has indicated a fault. Thus, they are advisory in nature and cannot, by themselves, cause a maintenance action. For this reason, the rationale for setting the decision threshold is to achieve a minimum 90% probability of correctly isolating the detected fault to a gear or bearing source, while minimizing the probability of falsely identifying a gear or bearing as the detected defect. 
     Percent Degradation Calculation (“PDC”)  84  is based upon past experience with stress wave analysis of bearing and gear faults for a broad range of high and low speed machines. In the preferred embodiment, PDC  84  assumes that a part is 100% degraded and at the end of its useful life (RED Limit) when its stress wave energy (“SWE”) rises to five times its baseline level. The baseline level (at a given sensor  30  location) can preferably be the mean value of IR readings obtained from baseline components. The 0% degradation point (YELLOW limit) is preferably chosen to be the mean plus three times the standard deviation of IR readings obtained from the baseline components. However, it should be understood that other values can be chosen for calculating the RED limit and YELLOW limit and are also considered within the scope of the invention. Thus, in the preferred embodiment the Percent Degradation equation is:          %                 Degradation     =         Measured                 SWE     -     YELLOW                 limit           RED                 Limit     -     YELLOW                 limit                                
     Preferably, percent degradation  87  is calculated only after test  81  of FDN  80   a  indicates the existence of a problem. Thus, the Percent (%) Degradation computation  84  uses the Mean and Standard Deviation of the SWE parameter obtained from baseline examples, at each sensor  30  location. The computation is concerned with where the current value of SWE sits, in the region between the Mean plus three Standard Deviations and five times the Means (a conservative Removal Limit). A negative result means that SWE is less than the Mean plus three Standard Deviations. A positive result less than one hundred means that SWE is between the Mean plus three Standard Deviations and five times the Mean. The following calculation can be used: 
     
       
         Degradation=(SWE−(Mean+3*Sigma/(5*Mean−(Mean+3*Sigma)) 
       
     
     Useful Life  86  is calculated using all available data for a Tail Number/DPU preferably up to 10 elapsed operating hours (“EOH”) previous to a Fault Indication. The data gives a series of (EOH, SWE) pairs which are used in a linear regression to calculate the approximate slope (m) and x-intercept (b) for the equation 
     
       
         
           y=mx+b, 
         
       
     
     where y is SWE and 
     x is EOH 
     The equation is preferably solved for the expected EOH when SWE is preferably five (5) times the pre-determined mean. The remaining useful life is the difference between the EOH and the current EOH of the system. 
     DPU  50  stores time domain features for a programmed operational monitoring period, such as approximately fourteen hours in one embodiment, before deleting old data to make room for the new data. Where no anomaly is detected, no data is downloaded from DPU  50  to LTC  96 . However, once an anomaly is indicated by DPU  50 , it is then downloaded to LTC  96  for further processing. The downloaded data is used by FDN  80   a,  FLN  80   b  and FIN  82 . Additionally, sensor(s)  30  responsible for triggering ADN  52  alarms will have their SWPT Time History also downloaded to LTC  96 . For these specific sensor(s)  30 , an FFT spectrum and amplitude distribution histograms can be plotted  89 . The FFT spectrum provides a visual picture of periodic friction/shock event amplitudes and frequencies. The amplitude distribution histogram is a graphic illustration of the lubrication effectiveness and the effects of fluid or particulate lube contamination. “Longer” trended data plots  88  can also be performed and can be useful in evaluating the effect of, or the need for, oil changes and corrective or preventive maintenance actions. 
     The trended stress wave energy data is preferably used to estimate the remaining useful life  86  of a component. In the preferred embodiment, remaining useful life  86  is estimated by the following: 
     (1) a linear regression analysis is performed on the last predetermined amount of data points (i.e. 120 data points which could represent ten hours of data) as a function of operating hours; 
     (2) the time of intersection of the linear function with the “RED” limit is calculated; and 
     (3) the current time is subtracted from the RED limit intersection time, and displayed by LTC  96  as Remaining Useful Life  86 . 
     Only IR data is used in percent degradation calculations  84 , trended data plots  88 , and remaining useful life estimates  86 . The SWPT FFT Spectra and Amplitude Distribution Histograms  89  can be generated from either IR data or Out of Regime (“OR”) data. 
     Summarizing the above, ADNs  52  detect abnormal features in the stress wave pulse train. FDNs  80   a  indicate the presence of damage, including single and multiple faults. FLNs  80   b  identify the assembly modules that contain faulty components. FINs  82  indicate specifically a bearing defect or a gear defect at the location identified by FLN  80   b.  RRN  56  is used for forecasting Remaining Useful Life  86  and % Degradation  87  determinations. 
     Where the present invention system  20  was used with a helicopter drive train, the monitored components included all of the primary drive train elements except the engines. In this example, fifteen sensor  30  locations were provided which included (1) port input module input housing; (2) port input module output housing; (3) starboard input module input housing; (4) starboard input module output housing; (5) main module planetary ring gear; (6) main module upper cover; (7) tail rotor output/rotor brake support bracket; (8) forward tail rotor shaft support bearing; (9) mid tail rotor shaft support bearing; (10) forward disconnect coupling support bearing; (11) aft disconnect coupling support bearing; (12) intermediate gearbox input; (13) intermediate gearbox output; (14) tail rotor gearbox input; and (15) tail rotor gearbox output. 
     In use and where five sensors  30  are provided for each DPU  50 , for each sensor  30  where DPU  50  indicates a possible anomaly, the following steps are preferably performed by system  20 : 
     (a) In Regime and Out of Regime data is obtained; 
     (b) The In Regime data is discarded if it indicates that the sensor  30  failed; 
     (c) The Out of Regime data is discarded if it indicates that the sensor  30  failed; 
     (d) If both the In Regime data and the Out of Regime data have not been discarded, then the time indicated by the worst of the In Regime data and Out of Regime data is used as a reference time to get preferably ten (10) cycle records including and prior to the reference time; 
     (e) If not, record indicates that a sensor  30  failed, then run each record through the Fault Detection Network; 
     (f) If 8 of the 10 records failed the FDN then mark this sensor  30  as a candidate for analysis; and 
     (g) return to (a) for the next sensor  30 . 
     Once all of the sensor  30  candidates have been found from the above steps, the following steps are performed by system  20 : 
     (a) the worst case from all sensor  30  candidates is chosen; 
     (b) the cycle record from the same time of the chosen sensor  30 &#39;s worst case data is selected and used for the following: 
     Run the PIM fault network; 
     Run the MM fault network; 
     Run the SIM fault network; 
     Run GEAR network for sensor  1   
     Run BEARING network for sensor  1   
     Run GEAR network for sensor  2   
     Run BEARING network for sensor  2   
     Run GEAR network for sensor  3   
     Run BEARING network for sensor  3   
     Run GEAR network for sensor  4   
     Run BEARING network for sensor  4   
     Run GEAR network for sensor  5   
     Run BEARING network for sensor  5   
     Evaluate Percent Degradation based on Sensor  1   
     Evaluate Percent Degradation based on Sensor  2   
     Evaluate Percent Degradation based on Sensor  3   
     Evaluate Percent Degradation based on Sensor  4   
     Evaluate Percent Degradation based on Sensor  5   
     Calculate Useful Life based on Sensor  1   
     Calculate Useful Life based on Sensor  2   
     Calculate Useful Life based on Sensor  3   
     Calculate Useful Life based on Sensor  4 . 
     Calculate Useful Life based on Sensor  5 . 
     The instant invention has been shown and described herein in what is considered to be the most practical and preferred embodiment for an example helicopter drive train. It is recognized, however, that departures may be made therefrom within the scope of the invention and that obvious modifications will occur to a person skilled in the art.