Coupling time evolution model with empirical regression model to estimate mechanical wear
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G06N-005/00
출원번호
US-0088683
(2011-04-18)
등록번호
US-8600917
(2013-12-03)
발명자
/ 주소
Schimert, James
Wineland, Arthur Ray
출원인 / 주소
The Boeing Company
대리인 / 주소
Ostrager Chong Flaherty & Broitman P.C.
인용정보
피인용 횟수 :
33인용 특허 :
10
초록▼
Mechanical systems wear or change over time. Data collected over a system's life can be input to statistical learning models to predict this wear/change. Previous work by the inventors trained a flexible empirical regression model at a fixed point of wear, and then applied it independently at time p
Mechanical systems wear or change over time. Data collected over a system's life can be input to statistical learning models to predict this wear/change. Previous work by the inventors trained a flexible empirical regression model at a fixed point of wear, and then applied it independently at time points over the life of an engine to predict wear. The embodiment disclosed herein relates those wear predictions over time using a time evolution model. The time evolution model is sequentially updated with new data, and effectively tunes the empirical model for each engine. The combined model predicts wear with dramatically reduced variability. The benefit of reduced variability is that engine wear is more evident, and it is possible to detect operational anomalies more quickly. In addition to tracking wear, the model is also used as the basis for a Bayesian approach to monitor for sudden changes and reject outliers, and adapt the model after these events.
대표청구항▼
1. A method for monitoring wear in a mechanical system, comprising: (a) repeatedly measuring a parameter over time during operation of the mechanical system;(b) calculating residuals at time points over the life of the mechanical system using an empirical model that models values of said parameter a
1. A method for monitoring wear in a mechanical system, comprising: (a) repeatedly measuring a parameter over time during operation of the mechanical system;(b) calculating residuals at time points over the life of the mechanical system using an empirical model that models values of said parameter as a function of values of other parameters, said residuals representing the respective differences between each measurement of said parameter and each corresponding parameter value predicted by the empirical model;(c) determining whether the measurements evolve as expected under a time evolution model that relates predictions of residuals over time; and(d) flagging an event in response to the measurements of said parameter deviating over time from the behavior predicted by the time evolution model by more than a threshold value,wherein in accordance with said time evolution model, wear at time t equals the wear at a previous time (t−1) plus a local growth rate at time (t−1). 2. The method as recited in claim 1, further comprising isolating and rejecting outliers and adapting the time evolution model to reflect the onset of a structural change in response to the measurements deviating over time from the behavior predicted by the time evolution model by more than said threshold value. 3. The method as recited in claim 1, wherein said adapting comprises increasing the uncertainty in a prior distribution as measured by a prior covariance matrix. 4. The method as recited in claim 1, further comprising updating the time evolution model in response to the measurements deviating over time from the behavior predicted by the time evolution model by less than said threshold value. 5. The method as recited in claim 4, wherein the local growth rate is a random walk. 6. The method as recited in claim 1, wherein said time evolution model is a second-order polynomial dynamic linear model. 7. The method as recited in claim 1, wherein step (c) comprises comparing the respective predictive abilities of standard and alternative time evolution models, wherein said alternative model is similar in form to said standard model, but allows for more extreme wear observations. 8. The method as recited in claim 1, further comprising isolating a fault in said mechanical system when said event is flagged. 9. The method as recited in claim 1, wherein said mechanical system is a gas turbine engine and said parameter is engine exhaust gas temperature. 10. A method for monitoring wear in a mechanical system, comprising: (a) repeatedly measuring a parameter over time during operation of the mechanical system;(b) calculating the value of a monitoring statistic for each of said measurements;(c) calculating the value of a cumulative monitoring statistic that is a product of sequential values of said monitoring statistic;(d) determining whether the value of said monitoring statistic is less than or not less than a threshold value;(e) determining whether the value of said cumulative monitoring statistic is less than or not less than said threshold value; and(f) flagging an event in response to the values of said monitoring statistic and said cumulative monitoring statistic being less than said threshold value,wherein said mechanical system is a gas turbine engine and said parameter is engine exhaust gas temperature. 11. The method as recited in claim 10, wherein said event is an abrupt structural change in said mechanical system. 12. The method as recited in claim 10, further comprising isolating and rejecting outliers included in said measurements and increasing the uncertainty in a prior distribution as measured by a prior covariance matrix to reflect the onset of a structural change in response to the values of said monitoring statistic and said cumulative monitoring statistic being less than said threshold value. 13. The method as recited in claim 10, further comprising updating a time evolution model in response to the values of said monitoring statistic and said cumulative monitoring statistic being not less than said threshold value. 14. The method as recited in claim 10, further comprising isolating a fault in said mechanical system when said event is flagged. 15. A system for monitoring the health of a mechanical system, comprising a computer system programmed to perform the following operations: (a) receiving values representing the results of measurements of a parameter over time during operation of the mechanical system;(b) predicting residuals at time points over the life of the mechanical system using an empirical model that models values of said parameter as a function of values of other parameters, said residuals representing the respective differences between each measurement of said parameter and each corresponding parameter value predicted by the empirical model;(c) determining whether the measurements evolve as expected under a time evolution model that relates predictions of residuals over time; and(d) flagging an event in response to the measurements of said parameter deviating over time from the behavior predicted by the time evolution model by more than a threshold value,wherein said mechanical system is a gas turbine engine and said parameter is engine exhaust gas temperature. 16. The system as recited in claim 15, wherein said computer system is further programmed to perform the following operations: isolating and rejecting outliers and adapting the time evolution model to reflect the onset of a structural change in response to the measurements deviating over time from the behavior predicted by the time evolution model by more than said threshold value. 17. The system as recited in claim 16, wherein said adapting comprises increasing the uncertainty in a prior distribution as measured by a prior covariance matrix. 18. The system as recited in claim 15, wherein said computer system is further programmed to update the time evolution model in response to the measurements deviating over time from the behavior predicted by the time evolution model by less than said threshold value. 19. The system as recited in claim 15, wherein step (c) comprises comparing the respective predictive abilities of standard and alternative time evolution models, wherein said alternative model is similar in form to said standard model, but allows for more extreme changes in the mechanical system or component. 20. A system for monitoring the health of a mechanical system, comprising a computer system programmed to perform the following operations: (a) receiving values representing the results of measurements of a parameter over time during operation of the mechanical system;(b) calculating the value of a monitoring statistic for each of said measurements;(c) calculating the value of a cumulative monitoring statistic that is a product of a plurality of values of said monitoring statistic;(d) determining whether the value of said monitoring statistic is less than or not less than a threshold value;(e) determining whether the value of said cumulative monitoring statistic is less than or not less than said threshold value; and(f) flagging an event in response to the values of said monitoring statistic and said cumulative monitoring statistic being less than said threshold value,wherein said mechanical system is a gas turbine engine and said parameter is engine exhaust gas temperature.
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이 특허에 인용된 특허 (10)
Abbott Terence S. (Williamsburg VA) Person ; Jr. Lee H. (Yorktown VA), Method and system for monitoring and displaying engine performance parameters.
Aragones, James Kenneth; Stein, Jeffrey William; Aragones, Amy Victoria; Tucker, William Talbert, System and method for automatically predicting the timing and costs of service events in a life cycle of a product.
Pomeroy,Bruce Douglas; Aragones,James Kenneth; Doganaksoy,Deniz Senturk, System and method for estimating turbine engine deterioration rate with noisy data.
Osborn, Brock Estel; Hershey, John Erik; Fullington, Michael Dean; Dockendorff, James Ernest; Herron, William Lee; Hansen, Carl Harold, System and method for trending exhaust gas temperature in a turbine engine.
Aragones,James Kenneth; Stein,Jeffrey William; Donoghue,Jeremiah Francis; Maruscik,Ronald George, System, method and computer product for baseline modeling a product or process.
Malta, Lucas R; Leao, Bruno Paes; Bittencourt, Jose Luiz; Orenstein, Leonardo Poubel, Using aircraft data recorded during flight to predict aircraft engine behavior.
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