Patent Publication Number: US-7725293-B2

Title: System and method for equipment remaining life estimation

Description:
BACKGROUND OF THE INVENTION 
   The present disclosure relates generally to remaining useful life prediction in prognosis, and particularly to equipment subsystem remaining useful life (RUL) prediction. 
   Estimating the remaining life of a subsystem is known in the art as prognostics. RUL estimates provide valuable information for operation of modern complex equipment. RUL estimates provide decision making aids that allow operators to change operational characteristics (such as load) which, in turn, may prolong the life of the subsystem. RUL estimates also allow planners to account for upcoming maintenance and set in motion a logistics process that supports a smooth transition from faulted to fully functioning equipment. Predicting remaining life is not straightforward because, ordinarily, remaining life is conditional on future usage conditions, such as load and speed, for example. Examples of equipment that may benefit from the use of remaining life estimates are aircraft engines (both military and commercial), medical equipment, and power plants, for example. 
   A common approach to prognostics is to employ a model of damage propagation contingent on future use. Such a model is often times based on detailed materials knowledge and makes use of finite element modeling. Because such models are extremely costly to develop, they must be limited to a few important parts of a system, but are rarely applied to subsystems. This approach is often called “lifting”. 
   Another approach is a data-driven approach to take advantage of time series data where equipment behavior has been tracked via sensor outputs during normal operation all the way to an end of equipment useful life. The end of equipment useful life may represent a totally non-functioning state of the equipment, for example, equipment failure. The end of equipment useful life can also represent a state of the equipment wherein the equipment no longer provides expected results. Alternatively, the end of useful life may be defined as when the equipment reaches a condition of imminent failure. When a reasonably-sized set of these observations exists, pattern recognition algorithms can be employed to recognize these trends and predict remaining life. These predictions are often made under the assumption of near-constant future load conditions. However, such run-to-end-of-equipment-useful-life data are often not available because, when the observed system is complex, expensive, and, safety is important, such as aircraft engines, for example, faults will be repaired before they lead to the end of equipment useful life. This deprives the data driven approach from information necessary for its proper application. 
   Another approach is a peer-based approach that utilizes information about other equipment to forecast the reliability of equipment within a fleet for the purpose of equipment selection to improve mission reliability. This approach typically focuses on the overall platform, such as a locomotive, or an aircraft, for example, without providing any prognostic insight regarding the components or sub-components of the platform. Furthermore, this approach assumes, in the ideal case, the availability of operational, maintenance, and environmental data for each platform. However, this information may not always be available. 
   Accordingly, there is a need in the art for a life estimation arrangement that overcomes these drawbacks. 
   BRIEF DESCRIPTION OF THE INVENTION 
   An embodiment of the invention includes a method to predict remaining life of a target. The method includes receiving information regarding a behavior of the target, and identifying from a database at least one piece of equipment having similarities to the target. The method further includes retrieving from the database data prior to an end of the equipment useful life, the data having a relationship to the behavior, evaluating a similarity of the relationship, predicting the remaining life of the target based upon the similarity, and generating a signal corresponding to the predicted remaining equipment life. 
   Another embodiment of the invention includes a system to predict remaining life of a target. The system includes a database comprising data for equipment, a processor in signal communication with the database, and a computational model application for executing on the processor, the computational model performing a method. The method includes receiving information regarding a behavior of the target, and identifying from the database at least one piece of equipment having similarities to the target. The method further includes retrieving from the database data prior to an end of the identified equipment useful life, the data having a relationship to the behavior, evaluating a similarity of the relationship, and predicting the remaining life of the target based upon the similarity. The processor is responsive to the computational model application to generate a signal corresponding to the predicted remaining equipment life. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Referring to the exemplary drawings wherein like elements are numbered alike in the accompanying Figures: 
       FIG. 1  depicts a schematic diagram of a prediction system in accordance with an embodiment of the invention; 
       FIG. 2  depicts a chart including a probe and peer equipment as spheres of data in accordance with an embodiment of the invention; 
       FIG. 3  depicts another chart including a probe and peer equipment as spheres of data in accordance with an embodiment of the invention; 
       FIG. 4  depicts a flowchart of an evolutionary algorithm in accordance with an embodiment of the invention; 
       FIG. 5  depicts a chart with results from an experimental test of a method to predict remaining probe equipment life in accordance with an embodiment of the invention; and 
       FIG. 6  depicts a flowchart of an exemplary method of predicting remaining probe equipment life in accordance with an embodiment of the invention. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   An embodiment of the invention will provide a peer-based approach that requires only operational data about other equipment to provide a remaining useful life forecast for a target piece of equipment, also herein referred to as a probe. An embodiment of the invention will provide a subsystem level prognostics approach over an entire subsystem without needing to assess the particular damage propagation mechanics. In an embodiment, the approach will operate in the absence of run-to-end-of-equipment-useful-life data. 
   In an embodiment of the invention, operational data from a plurality related equipment are collected and stored for monitoring and analysis purposes. Further, preliminary RUL estimates of the related equipment are collected from a variety of sources. In an embodiment, a fusion of a plurality of preliminary RUL estimates for at least one peer, or piece of related equipment having exhibited similar operational behavior to the probe, will be used to develop a Fuzzy Instance Model (FIM) to provide a preliminary RUL estimate of the target. 
   In an embodiment, these operational data represent the starting point of the preliminary RUL estimate of the probe. An embodiment of the invention uses local fuzzy models, which are related to both kernel regressions and locally weighted learning, to determine the remaining life of the probe. In an embodiment, the probe is a turbine engine. In an embodiment, the local fuzzy model is based on clusters of peers, or similar equipment with comparable operational characteristics and performance. In an embodiment, use of the local fuzzy model is distinguished from the development of individual models that are based only on the history of each engine, or the development of a global model that is based on the collective history of all the engines. An embodiment will include the combination of the fuzzy peer-based approach for performance modeling with an evolutionary framework for model maintenance. The evolutionary framework includes generating a collection of competing models, evaluating their performance in light of the currently available data, refining the best models using evolutionary search, and selecting the best model after a finite number of iterations. In an embodiment, the evolutionary framework is repeated to automatically update and improve the fuzzy model. In an embodiment, the best model at the end of the evolutionary process is used at run time to estimate RUL of the probe engine or engine subsystem. 
   As used herein, the term prognostics shall refer to the estimation of remaining useful subsystem life. The remaining useful life (RUL) estimates are in units of time or cycles of operation such as startup, flight, and shutdown of an aircraft engine, for example. The time estimate typically has associated uncertainty that is described as a probability density curve. Operators can choose a confidence level that allows them to incorporate a risk level into their decision making. Typically, the confidence interval on RUL estimates decreases as the prediction horizon decreases, such as near the end of component life, for example. 
   Prognostics is closely linked with diagnostics. As used herein, the term diagnostics shall refer to the detection of a fault condition, or an observed change in an operational state that is in related to a verifiable event. Faults are the first sign of a potential end of equipment useful life at some future time. An example of such a fault is an increase in engine fuel consumption resulting from a cracked turbine blade. The direct cost of the end of equipment useful life is unavoidable: ultimately, the component must be replaced. Moreover, there are indirect costs to the end of equipment useful life that are in many cases far greater than the cost of the repair. One source of indirect costs is secondary damage, for example, the end of the useful life of a component in the compressor stage of a gas turbine often causes damage to the rear stages. Another indirect cost is unscheduled maintenance. It is often less expensive to replace a faulty component during scheduled maintenance before it has reached the end of its useful life than to have a component reach the end of its useful life in the field and have to shut the whole system down. 
   In the absence of any evidence of damage or a faulted condition, prognostics reverts to statistical estimation of fleet-wide life, such as Weibull curves or other suitable mechanisms. It is more common to employ prognostics in the presence of an indication of abnormal wear, faults, or other non-normal situation. It is therefore important to include accurate and responsive diagnostics to provide a trigger point for the prognostic algorithms to operate. 
   Condition-based prediction systems depend on reliable fault diagnostics to initiate the prognostic algorithms. If diagnostics recognizes the start point of damage too late, the damage propagation models may lag reality and underestimate the damage. If prognostic algorithms are kicked off when there is no real damage, the benefit of a remaining life estimate is reduced. Accordingly, presence of an accurate diagnostic fault detection algorithm will be assumed as a basis for an embodiment of a prognostic RUL prediction. 
   Referring now to  FIG. 1 , a schematic diagram of an embodiment of a peer based prediction system  100  is depicted. In an embodiment, the prediction system includes at least one turbine engine  110 , at least one actual sensor  120 , a data transfer unit (DTU)  130 , a processor  140 , an interface unit  150 , a computer  160 , and a database  170 . The computer  160  further includes a program storage device  165 . 
   While an embodiment of the system has been described having at least one turbine engine, it will be appreciated that the scope of the invention is not so limited, and that the invention will also apply to prediction systems  100  including other pieces of equipment, such as locomotive engines, power generators, medical equipment, and rolling mills, for example. 
   In an embodiment, the at least one sensor  120  is disposed and configured to be responsive to an operating condition of the engine  110 , and to generate a signal representative of the operating condition of the engine  110 . In an embodiment, the at least one sensor  120  is in signal communication with the data transfer unit  130 , which makes available to the processor  140  the signal representative of the operating condition of the engine  110 . 
   In an embodiment, the processor  140  is in signal communication with an interface device  150 , such as to allow for an on-line monitoring process, as will be described further below. In an embodiment, the processor  140  is also in signal communication with the computer  160 . In an embodiment, the computer  160  is in signal communication with the database  170 . In an embodiment, the computer  160  is configured to make available to the database  170 , via the processor  140 , the data relating to the operating conditions of the engine  110 . In an embodiment, the database  170  is further configured to store and make available to the computer  160  the data relating to the operating conditions of the at least one engine  110 , including the signals generated by the at least one sensor  120 . The computer  160  also includes the program storage device  165  configured to store, and make available to the computer  160  for execution, a computational model for estimating the RUL of the probe. The processor  140  is responsive to the computational model application to generate a signal corresponding to the predicted remaining engine  110  life. It will be appreciated that the above is for illustration purposes only, and not for limitation of an embodiment of the invention. 
   While an embodiment of the invention has been described having a computer  160  in signal communication with the processor  140 , it will be appreciated that the scope of the invention is not so limited, and that the invention will also apply to prediction systems that have the computer  160  in direct signal communication with the data transfer unit  130 . It will be further appreciated that an embodiment of the invention will also include the computer in signal communication via the data transfer unit  130  via a variety of communication protocols, such as cellular, wireless internet, and others, for example, to allow an connection between the computer and the data transfer unit during use of the equipment, to enable a remote, on-line estimating process. 
   As used herein, instance-based reasoning (IBR) is used to describe a collection of previously experienced data that can be kept in their raw representation. IBR is distinguished from case-based reasoning (CBR), in which the data requires refinement, abstraction, and organization into cases. Like CBR, IBR is an analogical approach to reasoning, because it relies upon finding previous instances of similar operational behavior, and uses the data relating to the previous instances to create an ensemble of local models. Accordingly, the process to define the similarity is an important part of the performance of IBR models. It will be appreciated that similarity is a dynamic concept and will change over the use of the IBR model. In an embodiment, learning methodologies are employed to define and adapt the IBR model. Furthermore, the concept of similarity is not crisply defined, creating the need to allow for some degree of fuzziness in its evaluation. In an embodiment, evolving the design of a similarity function in conjunction with the design of the attribute space in which the similarity is evaluated will address the dynamic nature of the evaluation of similarity. 
   In an embodiment, the computational model, executing on the computer  160  of the prediction system  100  will predict the remaining life of the probe engine based on the following four steps, each of which will be described in turn, in further detail below. First, retrieval of operational data of peer equipment, also herein referred to as equipment, from the database  170 , the peer equipment having similarities to observed operational behavior of the probe. Second, evaluation of a similarity measure between the operational data of the probe and the retrieved operational data of the peer equipment. Third, creation of local models using the most similar peers, including a weighting value derived according to the similarity measures. Fourth, aggregation of outputs of the local models to estimate the RUL of the probe engine. 
   Retrieval of the data of peer equipment: Referring now to  FIG. 2 , a chart  200  depicts the probe  210  and a plurality of potential peer  220  equipment as spheres of data within a three dimensional space. In an embodiment, the retrieval step includes observing an operational behavior of the probe  210 , and identifying at least one piece of peer  221  equipment that has a similarity to the behavior of the probe  210 . In an embodiment, the plurality of potential peer  220  equipment can be considered to be points in an n-dimensional attribute space. For example, consider that the probe Q  210  has an associated n-dimensional vector of values x for each of n potential operational attributes, as shown by Equation-1.
 
Q=[x 1,Q ,x 2,Q , . . . , x n,Q ]  Equation-1
 
   A similar n-dimensional vector characterizes each of the potential peers u j    220  in the fleet with an attribute vector of values of x for each of n potential operational attributes of the peer u j    221  stored within the database  170  is shown by Equation-2.
 
u j =[x 1,j ,x 2,j , . . . , x n,j ]  Equation-2
 
   As used herein, the reference numeral  220  shall be used to indicate a grouping of peers, or peers in general, while the reference numeral  221  shall be used to indicate a single peer of interest. For each operational attribute of the probe Q  210 , denoted by a subscript dimension i, a Truncated Generalized Bell Function, TGBF i (x i ; a i ,b i ,c i )  230  is applied to define a range of the values of the attribute vector of x to include the potential peer  221  of the plurality of potential peers  220 . That is, the TGBF i    230  shall be used to determine if the potential peer  221  is similar to the probe  210 . In an embodiment, TGBF i    230  will be positioned along an axis  240  that denotes an independent variable value. In an embodiment, the TGBF i    230  will be centered at a value c i    250  equal to an independent variable value x i  of the probe  210 , along the axis  240 . In an embodiment, the TGBF i  represents the degree of similarity for the ith attribute, of the n potential attributes, along the ith independent axis. This is shown specifically in Equation-3, where a i  is a width parameter, b i  is a slope parameter, and ε is a truncation parameter. In an embodiment the truncation parameter ε=10 −5 . 
   
     
       
         
           
             
               
                 
                   
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   In an embodiment, the parameter c i    250  in each TGBF i    230 , for each of n attributes is equal to the value of that attribute of the probe Q  210 . Accordingly, each TGBF i    230  has two free parameters, the width parameter, a i    260  to control the spread of the TGBF i    230 , and the slope parameter, b i    270  to control curvature of the TGBF i    230 . In a coarse retrieval step, the peer  221  is retrieved from the database  170  if each operational attribute of the peer  221  is within the support, or between the axis  240  that denotes the independent variable and the TGBF i    230  of each operational attribute of the probe  210 . 
   In an embodiment, the retrieval step is formalized as follows. R i    280  is defined as half of the support of the TGBF i    230 , centered on the probe&#39;s coordinate x i,Q , or c i    250 . N(Q) is a neighborhood of Q, and defined by the constraint |x i,Q −x i,j |&lt;R i  where x i,Q  represents the value along the independent variable axis  240  of the probe  210 , and x i,j  represents the value along the independent variable axis  240  of the peer  221  for each operational attribute i. P(Q), the set of potential peers  221  of the probe Q  210  to be retrieved from the database, is therefore defined by all peers  221  within the range N(Q) from the value of Q: P(Q)={u j , j=1, . . . , m|u j εN(Q)}. 
   While an embodiment of the invention has been depicted in  FIG. 2  as retrieving potential peers with respect to one operational attribute in a three-dimensional space, depicted by the independent axis  240  labeled as x 1 , it will be appreciated that the scope of the invention is not so limited, and that the invention will also apply to retrieving potential peers with respect to more than one operational attribute, such as two, three, four, five, or more operational attributes, for example. While an embodiment of the invention has been depicted having two operational attributes in a three dimensional space, it will be appreciated that the scope of the invention is not so limited, and that the invention will also apply to peer based prediction systems that may utilize more than two operational attributes in a space having more than three dimensions, such as four, five, six, or more dimensions, for example. Further, while an embodiment has been shown having one TGBF with a set of parameters values a i ,b i  to retrieve potential peers with respect to a particular attribute, it will be appreciated that the scope of the invention is not so limited, and that the invention will also apply to peer prediction systems that use more than one TGBF, each TGBF defined with respect to a particular operational attribute i, and including an independent set of parameter values a i ,b i  to retrieve potential peers with respect to each particular operational attribute. 
   Evaluation of a similarity measure: Referring now to  FIG. 3 , a chart  300  depicts the probe  210  and a similar peer  222  equipment as spheres of data within a three dimensional space. An embodiment of the invention includes evaluating the similarity of the retrieved data of the similar peer  222  equipment. In an embodiment, each TGBF i    230  is a membership function representing the degree of satisfaction of each attribute, i of the constraint |x i,Q −x i,j |&lt;R i . It will be appreciated that chart  300  in  FIG. 3  includes two axes  310 ,  320  that each depict an independent variable value that has a value equal to an independent variable value of each of two attributes of the probe x i,Q    330 ,  335  and represent the degree of similarity along that dimension of the n potential operational attributes. That is, each TGBF i    230  measures the closeness of the similar peer  222  around the probe value x i,Q    330 ,  335  along the ith attribute, or independent axes  310 ,  320 . 
   In an embodiment, a similarity coefficient S i,j  of the relationship between the similar peer u j    222  and the probe  210  is evaluated via application of Equation-4 where x i,Q  represents a value of an attribute of the probe, and x i,j  represents a value of the attribute of the similar peer  222 . 
   
     
       
         
           
             
               
                 
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   In an embodiment, application of TGBF(x i,j ; a i ,b i ,x i,Q ) to each attribute will evaluate the similarity coefficient S i,j  between the similar peer  222  and the probe Q  210  along each attribute i.  FIG. 3  includes an axis  340  that depicts a dependent variable value along each attribute i. The TGBF  230  of each attribute i is centered on the value of that attribute of the probe  210 , such that the value of the TGBF for each attribute i of the probe  210  is equal to 1 along the axis  340 . 
   In an embodiment, determination of the c i    330 ,  335  for each TGBF is accomplished by projecting down to a point  350  onto a plane containing the two independent axes  310 ,  320  the location of the probe Q  210 . That point  350  is then projected to the two corresponding probe  210  values x i,Q    330 ,  335  of the two independent axes  310 ,  320 , which will become the center c i    330 ,  335  of each TGBF  230  of each attribute. In a similar fashion, the location of the similar peer  222  is projected down to a point  351  on the plane containing the two independent axes  310 ,  320 . That point  351  is then projected to the two corresponding similar peer  222  values x i,j    331 ,  336  of the two independent axes  310 ,  320 . Each of the peer x i,j    331 ,  336  values are projected to points  332 ,  337  of intersection with the respective TGBF  230 . The points of intersection  332 ,  337  are then projected to the axis  340 , to provide the corresponding similarity coefficient S i,j . It will be appreciated that in an embodiment as depicted in  FIG. 3 , a similarity coefficient S 1,j    360  has a value of approximately 0.8, and a similarity coefficient S 2,j    365  has a value of approximately 0.9. 
   In an embodiment, it is desired that the most similar peers  220  be the closest to the probe  210  along all n attributes. Accordingly, wherein the data of the similar peer  222  has a plurality of attributes, and each attribute has the similarity coefficient S i,j , a peer similarity coefficient, also herein referred to as an equipment similarity coefficient, S j  is defined as the intersection, or a minimum of the plurality of similarity coefficients S i,j , as shown in Equation-5.
 
S j =Min i=1   n {S i,j }=Min i=1   n {TGBF(x i ;a i ,b i ,x i,Q )}  Equation-5
 
   Equation-5 implies that each attribute is equally important in computing similarity. In an embodiment, each attribute is considered to have a different relevance in the computation of the peer similarity coefficient S j . Accordingly, a weighting value w i  is applied to each attribute value x i  and the peer similarity coefficient S j  is extended using a weighted minimum operator, as shown in Equation 6, where w i ε[0,1].
 
 S   j =Min i=1   n {Max[(1 −w   i ), S   j,i ]}=Min i=1   n {Max[(1 −w   i ),TGBF( x   i   ;a   i   ,b   i   ,x   i,Q ]}  Equation-6
 
   The set of values for the weights {w i } and of the parameters {(a i , b i )} are design choices that impact the proper selection of peers  220 . In an embodiment a manual setting of these values is used. In another embodiment, use of an evolutionary search to determine and refine these values, as will be described further below, is used. 
   Creation of local models: In an embodiment, m peers u j ,  220  (j=1 . . . m) are retrieved as similar to a given probe Q  210 . Each similar peer u j    222  has the peer similarity coefficient S j . In an embodiment, each similar peer u j    222  has an estimated RUL y j . In an embodiment, the estimated RUL y j  for each similar peer  222  is derived by at least one of: field observations leading to a distribution of remaining useful life; laboratory experiments measuring the wearing of the equipment; inspections of the equipment before major overhauls; statistical models that may be parametric or nonparametric, such as Weibull; and neural networks. 
   Aggregation of outputs: In an embodiment, individual RUL values y j  of each similar peer  222  are combined to generate the predicted RUL y Q  for the probe Q  210 . In an embodiment, this aggregation is defined as a similarity weighted average. In an embodiment, the similarity weighted average is evaluated by computing the weighted average of individual RUL estimates y j  of the similar peers  222  using their normalized similarity to the probe  210  as a weight, as shown in Equation-7, where j represents an index related to the peer, and, m represents the total number of peers included. 
   
     
       
         
           
             
               
                 
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   In an embodiment, a methodology to generate the best values of the weights {w i }, and search parameters {(a i , b i )} will enhance the accuracy of the RUL estimates. In an embodiment, the process of estimating RUL for one of the engine probe  210  and the engine component probe  210  includes an off-line training process and an on-line monitoring process. 
   In an embodiment, the off-line training process includes an evolutionary algorithm (EA) to tune and maintain parameters necessary to identify an optimal combination of values. In an embodiment, the parameters tuned by the EA include the width parameter, a i    260 , the slope parameter, b i    270 , and the weighting value w i . In an embodiment, the EA includes a population of individuals, also known as chromosomes, each of which includes a vector of elements that represent the distinct tunable parameters within the FIM configuration. In an embodiment, the chromosome has the form shown in Equation-8.
 
[w 1 w 2  . . . w n ][(a 1 ,b 1 ),(a 2 ,b 2 ), . . . (a n ,b n )]  Equation-8
 
   In an embodiment, each chromosome defines the TGBF  230  of the peer  221  within the attribute space and the relevance of each attribute in evaluating similarity via specification of a vector of weights [w 1 , w 2 , . . . w n ]. The second part of the chromosome, containing the vector of pairs [(a 1 ,b 1 ), (a 2 ,b 2 ) . . . (a n ,b n )] defines the parameters of the TGBF  230  for the attribute retrieval and similarity evaluation. If w i ε{0,1}, attribute selection is performed, that is, a crisp subset is selected from the universe of potential attributes. If w i ε[0,1] attribute weighting is performed, and a fuzzy subset of the universe of potential attributes is defined. 
   Referring now to  FIG. 4 , a flowchart  400  of an embodiment of the EA, known as a wrapper approach, or an optimization wrapper, is depicted. In an embodiment, the EA will compare, or test for accuracy, a sample model constructed with a sample chromosome against a known result. The EA will then adjust at least one of the weights and the attributes within the chromosome, retest, and monitor the effect on accuracy. In this way, the EA will search to find the optimum combination of weights and attributes within the chromosome to optimize the accuracy of the RUL prediction. 
   In an embodiment, the EA begins with inputting  410 , or receiving the complete set of attribute parameters, or historical data regarding the behavior of the peer  221  equipment. The process continues with encoding the parameters of the historical data for the EA, or defining  420  a chromosome of a subset of the attribute parameters, including corresponding weight values. The method proceeds by building  430  the Fuzzy Instance Model (FIM), using the weights and attributes provided by the chromosome. The FIM is then applied  440  to all but one of the peers  220  stored within the database  170 , to derive a predicted RUL, as disclosed herein. The method includes creating a performance metric, or fitness function f, and the predicted RUL ŷ is then tested  450 , or compared with the fitness function f, to evaluate the accuracy of the constructed model against a known result. Because it is desired to generate the most accurate RUL estimate, the absolute value of the prediction error, or prediction accuracy, is used as the function to minimize, i.e.: |y−ŷ|. Since the fitness function is to be maximized, the fitness function of the negation of such error is used, i.e.: ƒ=−|y−ŷ|. Subsequent to comparing the accuracy of the constructed model to the known result, the search continues  460  by defining  420  another chromosome of the subset of attribute parameters and repeating the process. 
   In an embodiment, the EA will be employed to maintain and refine the accuracy of the FIM. In an embodiment, the EA will adjust or tune attribute parameters a i    260  and bi  270  to alter the selection of potential peers  220  to optimize the fitness function. In an embodiment, the EA will be used to adjust or tune the weighting values w i  to alter the effect of a given attribute of the similar peer  222  on the FIM and optimize the fitness function. In an embodiment, the method includes storing the tuned parameters as the historical data for future use. It will be appreciated that optimum selection of the parameters  260 ,  270  and weighting values is dynamic, and may change over time depending upon the behavior of the probe  210 . Accordingly, in an embodiment, the EA will adjust the selection of at least one of the parameters  260 ,  270  and the weighting values in response to the operational behavior of the probe  210 . 
   In an embodiment, the on-line monitoring process will execute the FIM in response to the detection of faults. In an embodiment, in response to the detection of at least one fault, the n-dimensional vector of values, as defined by Equation-1 above (and shown below for convenience), is derived to represent the attribute state of the probe subsequent to the fault detection.
 
Q=[x 1,Q ,x 2,Q , . . . , x n,Q ]  Equation-1
 
   In an embodiment, specific diagnostic information, such as failure modes for example, can be incorporated in this attribute representation to improve the estimation performance. In an embodiment, the fault modes of the probe Q  210  are used to specify retrieval of the probe  221  to include a similar fault mode. The foregoing disclosed process is then followed to provide the RUL estimate for the probe Q  210  based on the peers  220  in the database  170 . In an embodiment, the n-dimensional vector of values described by Equation-1 for the probe  210 , as well as those described by Equation-2 for the peers  220 , are constantly updated with new operational data gathered. In an embodiment, RUL estimates are also updated with new operational data gathered from the probe Q  210 . In an embodiment, in response to a fault detection and a RUL prediction, the peer based system  100  can advise of control action, such as to lower engine speed for example, to increase the RUL of the faulted probe Q  210 . 
   Referring now to  FIG. 5 , a chart  500  depicting results from an experimental test of an embodiment of the peer based system  100  for an aircraft engine is depicted. An axis  510  depicts the RUL in cycles, and another axis  520  depicts the number of cycles subsequent to detection of the fault. A line  530  represents the true number of cycles remaining, and a line  540  represents the predicted RUL as provided by the peer based prediction system  100  disclosed herein. It will be appreciated that the accuracy of the predicted RUL  540  increases as the FIM is updated with operational data from each additional cycle. 
   In view of the foregoing, the peer based prediction system  100  performs the method of predicting remaining target equipment life. Referring now to  FIG. 6 , an embodiment of a generalized flowchart  600  of an exemplary method of predicting remaining target equipment life is depicted. 
   In an embodiment, the method begins with receiving  610  information regarding the operational behavior of the probe  210 , identifying  620  from the database  170  at least one peer  221  having operational similarities to the probe  210 , and retrieving  630  from the database  170  data prior to an end of the useful life of the peer  221 , the data having a relationship to the behavior of the probe  210 . The method further includes evaluating  640  the similarity of the relationship, predicting  650  the remaining life of the probe  210  based upon the similarity of the peer  221  and generating  660  the signal corresponding to the predicted remaining life of the probe  201 . In an embodiment, the receiving information  610  includes receiving information  610  regarding the behavior of the turbine engine  110  as the probe  210 . 
   An embodiment of the invention further includes applying the function to define the range for the attribute of the behavior. In an embodiment, the identifying  620  at least one peer  221  includes determining if the attribute of the operational data is within the defined range. In an embodiment, the function to define the range for the attribute of the behavior includes Equation-3. 
   
     
       
         
           
             
               
                 
                   
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                 Equation 
                 ⁢ 
                 
                   - 
                 
                 ⁢ 
                 3 
               
             
           
         
       
     
   
   In an embodiment, the method further includes applying the function to define the similarity coefficient, S i,j  of the attribute of the peer  221  data to the probe  210 . In an embodiment, the evaluating the similarity of the relationship includes using the similarity coefficient S i,j . In an embodiment, the function to define the similarity coefficient S i,j  includes Equation-4. 
   
     
       
         
           
             
               
                 
                   S 
                   
                     i 
                     , 
                     j 
                   
                 
                 = 
                 
                   
                     TGBF 
                     ⁡ 
                     
                       ( 
                       
                         
                           
                             x 
                             
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                   = 
                   
                     { 
                     
                       
                         [ 
                         
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                     } 
                   
                 
               
             
             
               
                 Equation 
                 ⁢ 
                 
                   - 
                 
                 ⁢ 
                 4 
               
             
           
         
       
     
   
   In an embodiment, the method further includes defining the peer similarity coefficient, S j  as the minimum of the plurality of similarity coefficients S i,j . In an embodiment, defining the peer similarity coefficient, S j  includes applying the weighting value to each similarity coefficient S i,j  of the plurality of similarity coefficients S i,j . 
   In an embodiment, the method further includes obtaining the local mathematical model of the remaining life estimate of the peer  221  from the database and applying the peer similarity coefficient S j  to the local model to predict the remaining life of the probe  210 . In an embodiment, the predicting  650  the remaining life of the probe  210  includes solving Equation-7. 
   
     
       
         
           
             
               
                 
                   y 
                   Q 
                 
                 = 
                 
                   
                     
                       ∑ 
                       
                         j 
                         = 
                         1 
                       
                       m 
                     
                     ⁢ 
                     
                         
                     
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                         j 
                       
                       × 
                       
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                         j 
                       
                     
                   
                   
                     
                       ∑ 
                       
                         j 
                         = 
                         1 
                       
                       m 
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       S 
                       j 
                     
                   
                 
               
             
             
               
                 Equation 
                 ⁢ 
                 
                   - 
                 
                 ⁢ 
                 7 
               
             
           
         
       
     
   
   An embodiment of the method also includes defining at least one of the weighting value, the width parameter, and the slope parameter with the evolutionary algorithm. 
   An embodiment of the method includes tuning and maintaining parameters of at least one of the identifying  620  at least one peer  222 , the retrieving  630  data, and the evaluating  640  the similarity of the relationship via the optimization wrapper. In an embodiment, the tuning and maintaining parameters includes receiving historical data regarding the behavior of the peer equipment  221 , encoding the parameters of the identifying  620 , retrieving  630 , and evaluating  640  for evolutionary algorithm, creating a performance metric for the predicting the remaining life of the probe  210 , tuning the parameters using the optimization wrapper to optimize the performance metric and storing the tuned parameters for future use and additional tuning. In an embodiment, the creating the performance metric includes defining the prediction accuracy measured as the absolute value of the prediction error. 
   An embodiment of the invention may be embodied in the form of computer-implemented processes and apparatuses for practicing those processes. Embodiments of the invention may also be embodied in the form of a computer program product having computer program code containing instructions embodied in tangible media, such as floppy diskettes, punch cards, CD-ROMs, hard drives, universal serial bus drives, or any other computer readable storage medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention. Embodiments of the invention may also be embodied in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits. A technical effect of the executable instructions is to predict a remaining useful life of equipment based upon remaining useful life estimates of similar pieces of peer equipment. 
   As disclosed, some embodiments of the invention may include some of the following advantages: the ability to estimate remaining useful life of equipment based on aggregating RUL estimates from selected peers; the ability to estimate remaining useful life of equipment with system operational data in the absence of detailed materials-based models, run-to-end-of-equipment-useful-life data, maintenance data, and environmental data; the ability to tune parameters for the selection of peers based on system operational data; the ability to improve peer selection by segmenting the operational data by failure modes; the ability to dynamically tune the model parameters based upon updated operational data; and the ability to use on-line equipment assessment to modify control action appropriately. 
   While the invention has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best or only mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. Also, in the drawings and the description, there have been disclosed exemplary embodiments of the invention and, although specific terms may have been employed, they are unless otherwise stated used in a generic and descriptive sense only and not for purposes of limitation, the scope of the invention therefore not being so limited. Moreover, the use of the terms first, second, etc. do not denote any order or importance, but rather the terms first, second, etc. are used to distinguish one element from another. Furthermore, the use of the terms a, an, etc. do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item.