Patent Publication Number: US-2011060569-A1

Title: Real-Time, Model-Based Autonomous Reasoner and Method of Using the Same

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application claims benefit of U.S. Provisional Application Ser. No. 61/275,883 filed Sep. 4, 2009, the entire disclosure of which is incorporated herein by reference. 
    
    
     FIELD OF THE DISCLOSURE 
     The present disclosure is generally related to prognostic reasoners and more particularly is related to real-time, model-based autonomous reasoners and methods of using the same. 
     BACKGROUND OF THE DISCLOSURE 
     Systems and system components are subject to degradation and failure after certain periods of time. As more industries evolve toward next generation electronic systems that replace traditional manually controlled systems, more components of systems are electronically controlled systems. Within the aviation industry, manually controlled aircraft are being replaced with fly-by-wire vehicles, and hydraulic and electro-hydrostatic actuators are replaced with their electro-mechanical counterparts. By eliminating fluid leakage problems in avionics, while reducing weight and enhancing vehicle control, feasibility and demand of electromechanical parts in avionic applications has been established. However, due to the inherent nature of the electronic components and system to fails, improved diagnostic and prognostic methods are sought to keep the all-electrical aircraft safe. 
     The same principle is applied throughout many industries. The need for greater efficiency and less mechanical problems within many industries and the systems in those industries is present. As more components of systems are replaced with electrical and electro-mechanical parts, the more prone the systems are to an electrical failure. When an electrical failure occurs, the system may not only endure down time, which is costly and hazardous, but some systems may cause resultant problems, such as significant safety hazards that could result in human injury or casualty. 
     Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies. 
     SUMMARY OF THE DISCLOSURE 
     Embodiments of the present disclosure provide a system and method for detecting and classifying in real-time a characteristic of a system component. Briefly described, in architecture, one embodiment of the system, among others, can be implemented as follows. The apparatus has at least one sensor positioned to sense the system component and output a first quantity of data corresponding to at least one characteristic of the system component. A computer modeler is in communication with the at least one sensor and receives the first quantity of data from the at least one sensor, wherein the computer modeler converts the first quantity of data into at least one numerical value and runs a computer model simulation. The computer model simulation models a present performance of the system component based on the at least one numerical value and compares the modeled present performance to an actual performance of the system component to detect an anomalous behavior of the system component. The detected anomalous behavior is optimized and expressed as a second quantity of data. An autonomous reasoner is in communication with the computer modeler wherein the autonomous reasoner collects the second quantity of data. A database is in communication with the autonomous reasoner and has a plurality of signatures related to predominant modes of the system component. The autonomous reasoner compares the second quantity of data with the signatures related to predominant modes of the system component in the database and identifies at least one signature related to predominant modes that substantially matches the second quantity of data. An output of the autonomous reasoner corresponds to the identified at least one signature, wherein the output is indicative of a cause of the at least one anomalous behavior of the system component. 
     The present disclosure can also be viewed as providing methods for detecting and classifying in real-time a characteristic of a system component. In this regard, one embodiment of such a method, among others, can be broadly summarized by the following steps: A method for detecting and classifying in real-time a characteristic of a system component, the method comprising the steps of: sensing the system component with at least one sensor; outputting from the at least one sensor a first quantity of data corresponding to at least one characteristic of the system component; receiving the first quantity of data at a computer modeler in communication with the at least one sensor; converting the first quantity of data into at least one numerical value; modeling a present performance of the system component based on the at least one numerical value; detecting an anomalous behavior of the system component by comparing the modeled present performance to an actual performance of the system component; optimizing the detected anomalous behavior and expressing it as a second quantity of data; collecting the second quantity of data with an autonomous reasoner in communication with the computer modeler; comparing the second quantity of data with a plurality of signatures related to predominant modes of the system component stored in a database in communication with the autonomous reasoner; identifying at least one signature related to predominant modes that substantially matches the second quantity of data; and outputting an output of the autonomous reasoner corresponding to the identified at least one signature, wherein the output is indicative of a cause of the at least one anomalous behavior of the system component. 
     The present disclosure can also be viewed as an apparatus for detecting and classifying in real-time a characteristic of a system component. Briefly described, in architecture, one embodiment of the system, among others, can be implemented as follows. The apparatus contains at least one sensor positioned to sense the system component and output a first quantity of data corresponding to at least one characteristic of the system component. A computer modeler is in communication with the at least one sensor and receives the first quantity of data from the at least one sensor, wherein the computer modeler runs a computer model simulation of the system component to detect an anomalous behavior of the system component, wherein the detected anomalous behavior is optimized and expressed as a second quantity of data. An autonomous reasoner is in communication with the computer modeler wherein the autonomous reasoner collects the second quantity of data. A database is in communication with the autonomous reasoner, the database having a plurality of signatures related to predominant modes of the system component, wherein the autonomous reasoner compares the second quantity of data with the signatures related to predominant modes of the system component in the database and identifies at least one signature related to predominant modes that substantially matches the second quantity of data. An output of the autonomous reasoner corresponds to the identified at least one signature, wherein the output is indicative of a cause of the at least one anomalous behavior of the system component. 
     Other systems, methods, features, and advantages of the present disclosure will be or become apparent to one with skill in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, methods, features, and advantages be included within this description, be within the scope of the present disclosure, and be protected by the accompanying claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Many aspects of the disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views. 
         FIG. 1  is a schematic illustration of an apparatus for detecting and classifying in real-time a characteristic of a system component, in accordance with a first exemplary embodiment of the present disclosure. 
         FIG. 2  is a schematic illustration of an apparatus for detecting and classifying in real-time a characteristic of a system component, in accordance with the first exemplary embodiment of the present disclosure. 
         FIG. 3  is a schematic illustration of an apparatus for detecting and classifying in real-time a characteristic of a system component, in accordance with a second exemplary embodiment of the present disclosure. 
         FIG. 4  is a graph of a following error of an electro-mechanical actuator with just a single-aged MOSFET, in accordance with example 1. 
         FIG. 5  is a schematic diagram of a MATLAB/Simulink model of an electro-mechanical actuator, in accordance with example 1. 
         FIG. 6  is a flowchart illustrating a method for detecting and classifying in real-time a characteristic of a system component in accordance with the first exemplary embodiment of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a schematic illustration of an apparatus  10  for detecting and classifying in real-time a characteristic of a system component  20 , in accordance with a first exemplary embodiment of the present disclosure. The apparatus  10  includes at least one sensor  30  positioned to sense a system component  20  and output a first quantity of data corresponding to at least one characteristic of the system component  20 . The system component  20  may be any type of device having a measurable property and is located within a system  24 . A computer modeler  40  is in communication with the sensor  30  and receives the first quantity of data from the sensor  30 . The computer modeler  40  converts the first quantity of data into at least one numerical value and runs a computer model simulation. The computer model simulation models a present performance of the system component  20  based on the numerical value and compares the modeled present performance to an actual performance of the system component  20  to detect an anomalous behavior of the system component  20 . The detected anomalous behavior is optimized and then expressed as a second quantity of data. 
     An autonomous reasoner  60  is in communication with the computer modeler  40  and collects the second quantity of data. A database  50  is in communication with the autonomous reasoner  60  and has a plurality of signatures  52  related to predominant modes of the system component  20 . The autonomous reasoner  60  compares the second quantity of data  62  with the signatures  52  related to predominant modes of the system component  20  in the database  50  and identifies at least one signature  52  related to predominant modes that substantially matches the second quantity of data  62 . An output  70  of the autonomous reasoner  60  corresponds to the identified at least one signature  52 , wherein the output  70  is indicative of a cause of the at least one anomalous behavior of the system component  20 . 
     The apparatus  10  may be used with a variety of different types of system components  20  on a variety of systems  24 . The systems  24  may include any type of system, machine, device or series of devices that uses computerized, mechanical, electrical and/or electro-mechanical components. This may include systems  24  such as aircrafts, watercrafts, trains, vehicles, robotic machines, programmable machines, industrial tools or any other type of system  24  subject to component degradation. As is illustrated in  FIG. 1 , the apparatus  10  is primarily discussed with regards to the avionics industry, as aircraft have many components subject to degradation and/or failure over time. As one having skill in the art can see, the apparatus  10  may be used in any industry that utilizes systems  24  capable of degradation, wear and/or failure, all of which are considered within the scope of this disclosure. 
     The system component  20  may include a number of different types of devices used on, within, or in connection to the system  24 . The system component  20  may include components that are computerized, mechanical, electrical and/or electro-mechanical components, or any combination thereof. The system component  20  may be any other device that has a measurable property, that has one or more equations that can be written to govern or describe its operation and is subject to degradation, wear and/or failure, in one or more ways. For example, as illustrated in  FIG. 1 , the system component  20  may be an actuator or a target flight control system used with an aircraft, wherein the response time or movement of the actuator is measurable. In another example, the system component  20  may be an electrical part on a machine, such as a transistor, a capacitor, and/or any other element that is part of a power drive stage of an electro-mechanical device, wherein the physical degradation of the device can be measured and/or modeled. 
     At least one sensor  30  is positioned to sense the system component  20  and compile a first quantity of data from the system component  20 . The sensor  30  may sense the system component  20  by monitoring communications of the system  24  of a bus or similar data transmission system, and retrieve data from one or more signals transmitted over the bus. For example, the sensor  30  could be a bus monitor located on the system  24 . The sensor  30  may also create or output a sensor output that is stored or collected from the sensor  30 . This stored sensor output may include historical operation data, such as historical flight data or any other type of data that is stored as post real-time data. The stored sensor output may be the same or different from the first quantity of data, and may depend on the type of system  24  that the apparatus  10  is used with. The computer modeler  40  may optimize the detected anomalous behavior at least partly based on the stored sensor output, thereby taking into considerations historical operational information of the system  24  when optimizing the detected anomalous behavior. 
     The first quantity of data may correspond to at least one characteristic of the system component  20 . The at least one characteristic of the system component  20  may include any number or type of characteristic associated with the system component  20 , such as a characteristic related to a degradation state of the system component  20 , a characteristic related to an environmental condition or physical condition of the system  24  or system component  20 , and/or a characteristic related to another working state of the system component  20 . Other characteristics may include position, trajectory and/or any other detectable condition, or combination thereof. The first quantity of data corresponding to the at least one characteristic of the system component  20  is output from the sensor  30 . The first quantity of data may be expressed in a number of ways, including in one coordinate, in two coordinates or in more than two coordinates, and the quantity of data may be visually graphed on one or more axes. 
     The computer modeler  40  receives the first quantity of data from the sensor  30  and converts the first quantity of data into at least one numerical value. This may include converting the first quantity of data into any quantity of numbers having any format, including numbers expressed with variables and/or by equation. The computer modeler  40  may run a computer model simulation to model a present performance of the system component  24 . This may be based on, at least in part, the numerical value that the first quantity of data is converted into, and/or any equation derived therefrom. The computer modeler  40  may compare the modeled present performance to an actual performance of the system component  20 . The modeled present performance may describe how the system component  20  should function and the actual performance of the system component  20  may describe how the system component  20  is actually function. The computer modeler  40  may detect an anomalous behavior of the system component  20  based on the comparison. An anomalous behavior may commonly be detected when the modeled present performance and the actual performance of the system component  20  do not substantially match. 
     Any anomalous behavior that is detected may be optimized. The computer modeler may adjust, or optimize any anomalous behavior to accurately represent the values and the systems parameters at that point in time for that performance. Optimizing the anomalous behavior may also be understood and disclosed as optimizing the first quantity of data, since the optimization may occur at any time within the computer modeler  40  prior to the second quantity of data being sent to the autonomous reasoner  60 . Optimizing the anomalous behavior may include adjusting a quantity of pre-specified variables until a substantially suitable match between the optimizing model and the present performance is reached. Once completed, a second quantity of data may be found, or an iteration limit may be reached. When an iteration limit is reached, there may be no substantially suitable match available. Optimizing the anomalous behavior may be done electronically within the computer modeler  40 , or manually with one or more knobs or adjustment controls, or any combination thereof. 
     The autonomous reasoner  60  may be in communication with at least the database  50  and the computer modeler  40 . This communication, as well as any other communication between components in the apparatus  10  may be facilitated by any communication connection, such as a wired communication connection or a wireless communication connection, as is discussed further with regards to  FIG. 2 . The autonomous reasoner  60  may receive or collect the second quantity of data from the computer modeler  40 . Then the second quantity of data may be compared with a list of criteria in the autonomous reasoner and if the second quantity of data fit one or more of the criteria then the resulting failure mode that corresponds to those criteria is outputted from the reasoner to a system that can present, or not present, the results. 
     The database  50  may be in communication with the autonomous reasoner  60 , the computer modeler  40 , or any other component by any type communication connection. For example, the database  50  may be integral with the autonomous reasoner  60 , such as a hard drive that is directly connected to the autonomous reasoner  60 . Additionally, the database  50  may be located remote from the autonomous reasoner  60  and accessible from a communication connection such as a network, an Internet connection, a dedicated wireless band or any other type of connection. In  FIG. 1 , only one database  50  is illustrated, but any number of databases  50  may be included. Furthermore, the database(s)  50  may be keyed to specific systems  24 , specific system components  20 , or specific characteristics of the system component  20 . For example, there may be one or more databases  50  for each system component  20  within the system  24  or one or more databases  50  for each system  24 . The database  50  may also be keyed to a particular characteristic or operation of a plurality of system components  20 , as many system components  20  may use similar parts that are subject to degradation in similar ways. 
     The database  50  has a plurality of signatures  52  related to predominant modes of the system component  20 . The signatures  52  may be considered a string of data and may be expressed in any number of coordinates and/or dimensions. To those having skill in the art, the database  50  may be considered a dictionary or a library of signatures  52 . Each of the signatures  52  is related to predominant modes of the system component  20 . In other words, each of the signatures  52  represents a state of the system component  20 , such as a degradation state, a failing state, a successfully working state, or any combination thereof. As one skilled in the art can see, a vast number of signatures  52  may be included in the database  50  to account for the vast number of possibilities of the state of the system component  20 . 
     The autonomous reasoner  60  may compare the second quantity of data with one or more of the signatures  52  related to predominant modes of the system component  20  in the database  50 . The autonomous reasoner may identify at least one signature  52  that is related to one or more predominant modes that substantially matches the second quantity of data. A signature  52  that substantially matches the second quantity of data may include a signature  52  that is closely identical to at least a portion of the second quantity of data, but may also include a signature  52  that is approximately similar to, in at least one way, the second quantity of data. The autonomous reasoner  60  may then produce an output  70  of the identified signature  52 . 
     Practically speaking, the second quantity of data may be compiled as a series or list of numbers that the autonomous reasoner  60  compares to signatures  52  in a numerical format. However, one skilled in the art will understand that the characteristic of the system component  20  that the second quantity of data corresponds to may be visually identifiable graphically. For example, the second quantity of data may include a measurement of movement of a system component  20 , such as an airplane wing-flap actuator, over a given period of time. The database  50  may include a plurality of signatures  52  that represent when the airplane wing-flap actuator is performing correctly, or when the airplane wing-flap actuator has experienced some level of degradation. As the movement of the airplane wing-flap actuator is recorded over the given period of time, the second quantity of data may include the characteristic identified by a portion of the second quantity of data, or a series of patterns within the second quantity of data. When the second quantity of data is compared to the signatures  52 , the autonomous reasoner  60  may identify one or more signatures  52  that include a characteristic that substantially matches the characteristic identified in the second quantity of data. The signature  52  may further correspond to an identifiable type of degradation of the system component  20 . 
     Once the at least one signature  52  is identified by the autonomous reasoner  60 , the autonomous reasoner  60  produces an output  70  corresponding to the identified at least one signature  52 . The output  70  may commonly be an electronic message that is communicated to a system computer, an operator of the system  24 , any component within the system  24 , a database and/or a third party, such as a remote control station. However, the output  70  may be given in any format and may be directed to any computer, person or entity. The output  70  includes information that is indicative of a cause of the at least one characteristic of the system component  20 . In other words, the output  70  may identify which at least one signature  52  substantially matches the characteristic identified in the second quantity of data, and then provide further information on what is causing the characteristic within the system component  20 . 
     The output  70  may be indicative of the cause of the at least one characteristic of the system component  20  in a variety of ways, depending on the design of the apparatus  10  and its intended use. For example, the output  70  may provide specific information on what is causing the characteristic, such as detailing which part of the system component  20  is malfunctioning or operating outside of normal operation parameters. If the system component  20  is an electrically driven motor, the output  70  may provide information that a certain coil within the electrically driven motor is causing the characteristic. The output  70  may also provide a general indication of the cause of the characteristic within the system component  20 , such as by indicating the operation of the system component  20  (i.e., failed or working), or by providing a percentage determination of the operating status of the system component  20  (i.e., 50% working or 75% failed). As the characteristic may include a variety of causes, the output  70  may provide more than one indication of the cause. 
     The apparatus  10  may be designed to provide a passive response to the characteristic of a system component  20 , an active response to the characteristic of the system component  20  or a combination thereof. The apparatus  10  may be provided as a separate unit to an existing system  24 , or may be embedded within the system  24  or a fully integrated system-on-chip commercial solution. The apparatus  10  allows for an early warning of incipient fault conditions of the system component  20 , which may allow for the system component  20  to be fixed in a timely manner. This, in turn, will lead to better maintenance of the system  24 , which provides for a safer use of the system  24  and a more reliable system  24 . 
     Furthermore, the apparatus  10  is fully modifiable to allow it to be used with a wide range of systems  24 . For example, the apparatus  10  may be modified with a set of physics equations that are written in terms of the symbolic variables that describe the system and estimates for the variables values must be known. The prototype provide a graphical user interface (GUI) that asks for the equations in symbolic state-space form and then provides a place for every variable to be estimated and have upper and lower limits set; these limits constrain the solver to realistic values. Another important aspect that the apparatus  10  includes is the ability to effectively decouple the autonomous reasoner  60  from the system component  20 , which thereby allows the autonomous reasoner  60  the ability to support multiple systems  24  or multiple system computers  80  (see  FIG. 2 ). Using one autonomous reasoner  60  for multiple systems  24  may simplify adoption, validation, integration, and support of the apparatus  10 . Potentially, a single autonomous reasoner  60  may monitor multiple systems  24  and multiple system components  20 , which may optimize overall sensor costs. The autonomous reasoner  60  may be implemented with a number of software systems and/or field upgradeable firmware that would be capable of supporting evolving interface standards and prognostic health measurement capabilities. 
       FIG. 2  is a schematic illustration of an apparatus for detecting and classifying in real-time a characteristic of a system component  10 , in accordance with the first exemplary embodiment of the present disclosure. The apparatus  10  may include a number of other features to enhance convenience, usefulness and operation of the apparatus  10 . For example, the system  24  may include a system computer  80 , which is programmed to at least partially control the system component  20 , among other components on the system  24 . The system computer  80  may be a flight control computer onboard an aircraft, as is shown in  FIG. 2 , or any other type of system computer  80  on any other system  24 . 
     The output  70  of the autonomous reasoner  60  may be communicated to the system computer  80 , which may then process the output  70  in a number of ways. The system computer  80  may adjust a control of the system component  20  based on the indicated cause of the at least one characteristic of the system component  20 . For example, if the system component  20  is a wing-flap actuator that is not responding fully to commands, the output  70  may indicate that the cause of the problem is a faulty actuator motor. The system computer  80  may adjust future commands given to the wing-flap actuator to account for the faulty actuator motor to allow for operation of the system  24  until the faulty actuator motor can be fixed or replaced. Accordingly, the system computer  80  adjusting control of the system component  20  may allow an operator of the system  24  to continue to operate the system  24  as if the system component  20  was working correctly. 
     In addition to making an adjustment of control over the system component  20 , the system computer  80  may also notify an operator, a computer, or another entity of the cause indicated by the output  70 . This may make the operator or other entity aware of the cause, even if the system computer  80  does not need to adjust control of the system component  20 . This may include a notification of a system  24  error, a notification of a system component  20  error, a notification of adjustment of control of the system component  20 , a notification of the output  70  of the autonomous reasoner  60  and/or a recommendation for future operation of the system  24 , or any combination thereof. As one having skill in the art can see, an operator of the system  24  having knowledge of a system component&#39;s  20  failure, state of degradation or other working status state, may allow the operator, such as a pilot or air traffic controller, to operate the system  24  more safely. The operator may account for the system component  20  failure or present working state, and make manual adjustments, if need be. The system computer  80  may also log or store any outputs  70  received from the autonomous reasoner  60  within a local or remote database. 
       FIG. 3  is a schematic illustration of an apparatus  110  for detecting and classifying in real-time a characteristic of a system component  120 , in accordance with a second exemplary embodiment of the present disclosure. The apparatus  110  for detecting and classifying in real-time a characteristic of a system component  120 , is substantially similar to the apparatus  10  of the first exemplary embodiment. The apparatus  110  includes one autonomous reasoner  160  that may be in communication with multiple sensors  130 . In  FIG. 3 , the multiple sensors  130  are illustrated being located in multiple systems  124 , although the multiple sensors  130  may also be located within one system  124 . The communication connection between the autonomous reasoner  160  and the systems  124  may be a wireless communication connection, whereby multiple first quantities of data are first transmitted through one or more wireless communication systems to one or more computerized modelers  140 . Additionally, the autonomous reasoner  160  may be in communication with one or more databases  150 , and the autonomous reasoner  160  may produce an output  170  that may be communicated via a wired or wireless connection. 
     Although the principle operation of the apparatus  110  is similar to the operation of the apparatus  10  of the first exemplary embodiment, the apparatus  110  may include a different architecture for operation. For example, the apparatus  110  may include a variety of system components  120  that are sensed by one or more sensors  130 , which output many first quantities of data to the computer modeler  140 , which may output many second quantities of data to the autonomous reasoner  160 . Practically, the autonomous reasoner  160  may be located in a stationary position, but may be located proximate to or remote from the system  124 , depending on what type of system  124  is present. If the system  124  is an aircraft, then the autonomous reasoner  160  may be located within an airport or central operation center that is hundreds or thousands of miles from the aircraft. If the system  124  is an industrial machine in a factory, then the autonomous reasoner  160  may also be conveniently located within the factory. In addition, the autonomous reasoner  160  may be located with a manufacturer of the system  124 , or in a location proximate to and/or accessible to a maintenance provider. 
     Example 1 
     Example 1 provides an illustration of the apparatus for detecting and classifying in real-time the characteristic of a system component in accordance with the first and second exemplary embodiments. The example uses an aircraft as the system and an electro-mechanical actuator as the system component. 
       FIG. 4  is a graph  200  of a following error  210  of an electro-mechanical actuator with just a single-aged MOSFET, in accordance with Example 1. The following error  210  in an electro-mechanical actuator reveals a great deal about its health. Following error  210  may be an indication of the variance between a commanded position and an actual position of the associated control surface. The following error  210  may be directly correlated with physical degradation of the metal-oxide-semiconductor field-effect transistor (MOSFET), capacitors, and other elements that comprise the power drive stage of the electro-mechanical actuator. The following error  210  may indicate how an anomalous electromechanical actuator operation just prior to catastrophic failure provided helpful insight into the failure mechanism. Many symptoms of an electro-mechanical actuator, electronic power system, and MOSFET can be observed from the following error and modeled in a modeling program, like a MATLAB/Simulink program. 
       FIG. 5  is a schematic diagram of a MATLAB/Simulink model  300  of an electro-mechanical actuator, in accordance with Example 1. With reference to  FIG. 5 , a brushless direct-current (BLDC) motor model  310  is connected to an H-bridge power stage  320  including the MOSFET switches. A back electromotive force (EMF) sensing block  330  is connected to the H-bridge power stage  320 . A pulse width modulation signal generator  340  is connected to the EMF sensing block  330 , and a variable DC-link voltage control block  350 , which comprises a position control, a speed control, and a present control in a row. The variable DC-link voltage control block  350  receives a created reference signal from a plurality of signal builders  360 , located within a hardware interface  365 . The signal builders may be at least one of a “Position Signal”, “Speed Signal”, or “Current Signal”. A reference signal  370  may be fed to the variable DC-link voltage control block  350 , which compares the reference signal  370  with every actual signal  372  coming from the BLDC motor model  310 . For example, the BLDC motor model  310  and the system may have the same inputs but may return different results to the same command, which can be detected through comparison. Accordingly, the comparison of the reference signal  370  and the actual signal  372  may be used to determine if there is the characteristic within the electromechanical actuator. 
       FIG. 6  is a flowchart  400  illustrating a method for detecting and classifying in real-time a characteristic of a system component  20  in accordance with the first exemplary embodiment of the disclosure. It should be noted that any process descriptions or blocks in flow charts should be understood as representing modules, segments, portions of code, or steps that include one or more instructions for implementing specific logical functions in the process, and alternate implementations are included within the scope of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present disclosure. 
     As is shown by block  402 , the system component  20  may be sensed with at least one sensor  30 . The at least one sensor  30  may output a first quantity of data corresponding to at least one characteristic of the system component  20  (Block  404 ). The first quantity of data may be received at a computer modeler  40  in communication with the at least one sensor  30  (Block  406 ). The first quantity of data may be converted into at least one numerical value (Block  408 ). A present performance of the system component  20  may be modeled based on the at least one numerical value (Block  410 ). An anomalous behavior of the system component  20  may be detected by comparing the modeled present performance to an actual performance of the system component (Block  412 ). The detected anomalous behavior may be optimized and expressed as a second quantity of data (Block  414 ). The second quantity of data may be collected with an autonomous reasoner  60  in communication with the computer modeler  40  (Block  416 ). The second quantity of data may be compared with a plurality of signatures  52  related to predominant modes of the system component stored in a database  50  in communication with the autonomous reasoner  60  (Block  418 ). At least one signature  52  related to predominant mode that substantially matches the second quantity of data may be identified (Block  420 ). An output  70  of the autonomous reasoner  60  corresponding to the identified at least one signature  52  may be produced, wherein the output  70  is indicative of a cause of the at least one anomalous behavior of the system component  20  (Block  422 ). 
     A number of other steps may be included with the method, as disclosed herein. For example, the autonomous reasoner  60  may be connected, either with a wired connection or a wireless connection, with at least one of the at least one sensor  30  and the database  50 . Additionally, the method may include the steps of at least partially controlling the system component  20  with a system computer; communicating the output  70  of the autonomous reasoner  60  to the system computer; and adjusting a control of the system component  20  based on the indicated cause of the at least one characteristic of the system component  20 . A communication may be sent from the system computer to an operator of a system  24  in which the system component  20  is located, wherein the communication includes at least one of a notification of a system  24  error, a notification of a system component  20  error, a notification of adjustment of control of the system component  20 , a notification of the output  70  of the autonomous reasoner  60  and a recommendation for future operation of the system  24 . 
     Additionally, a plurality of system components  20  may be provided. The plurality of system components  20  may be sensed with the at least one sensor  30 , and a second quantity of data corresponding to at least one characteristic for each of the plurality of system components  20  may be output  70  from the at least one sensor  30 . The plurality of system components  20  may be housed within a single system  24 , or in a plurality of independent systems  24 . When the second quantity of data is output, it may be substantially matched with at least one of the plurality of signatures  52  related to predominant modes of the system component  20 , which may correspond to at least one state of degradation of the system component  20 . The second quantity of data and/or the signature  52  may be graphically modeled in at least two coordinates. 
     It should be emphasized that the above-described embodiments of the present disclosure, particularly, any “preferred” embodiments, are merely possible examples of implementations, merely set forth for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above-described embodiments of the disclosure without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure and the present disclosure and protected by the following claims.