Applying the General Path Model to Estimation of Remaining Useful Life
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Published
Jan 1, 2011
Jamie Coble
J. Wesley Hines
Abstract
The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. This class of prognostic algorithms is termed effects-based, or Type III, prognostics. A unit-specific prognostic model, called the General Path Model, involve identifying an appropriate degradation measure to characterize the system's progression to failure. A functional fit of this parameter is then extrapolated to a pre-defined failure threshold to estimate the remaining useful life of the system or component. This paper proposes a specific formulation of the General Path Model with dynamic Bayesian updating as one effects-based prognostic algorithm. The method is illustrated with an application to the prognostics challenge problem posed at PHM '08.
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Keywords
PHM
References
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Chinnam, R.B. (1999), "On-line Reliability Estimation of Individual Components, Using Degradation Signals", IEEE Transactions on Reliability, Vol 48, No 4, pp. 403-412.
Garvey, D. and J.W. Hines (2006), “Robust Distance Measures for On-Line Monitoring: Why Use Euclidean?” 7th International Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS) Conference on Applied Artificial Intelligence.
Garvey, J., R. Seibert, D. Garvey, and J.W. Hines (2007), "Application of On-Line Monitoring Techniques to Nuclear Plant Data," Nuclear Engineering and Technology, Vol. 39 No. 2.
Gelman, A., J. Carlin, H. Stern, and D. Rubin (2004), Bayesian Data Analysis 2nd ed. Boca Raton: Chapman and Hall/CRC.
Girish, T., S.W. Lam, J.S.R. Jayaram (2003), "Reliability Prediction Using Degradation Data – A Preliminary Study Using Neural Network-based Approach," Proc. European Safety and Reliability Conference (ESREL 2003), Maastricht, The Netherlands, Jun. 15-18.
Heimes, F.O. (2008), “Recurrent Neural Networks for Remaining Useful Life Estimation,” PHM '08, Denver CO, Oct 6-9.
Hines, J.W., D. Garvey, R. Seibert, A. Usynin, and S.A. Arndt, (2008) “Technical Review of On-Line Monitoring Techniques for Performance Assessment, Volume 2: Theoretical Issues,” NUREG/CR-6895 Vol 2.
Hines, J.W., J. Garvey, J. Preston, and A. Usynin (2007), “Empirical Methods for Process and Equipment Prognostics,” Reliability and Maintainability Symposium RAMS.
Koppen, M. (2004), “No-Free-Lunch Theorems and the Diversity of Algorithms”, Congress on Evolutionary Computation, 1, pp 235 – 241.
Kothamasu, R., S.H. Huang, and W.H. VerDuin (2006), “System Health Monitoring and Prognostics – A Review of Current Paradigms and Practices,” International Journal of Advanced Manufacturing Technology 28: 1012 – 1024.
Lindely, D.V. and A.F. Smith (1972), "Bayes Estimates for Linear Models," Journal of the Royal Statistical Society (B), Vol 34, No 1, pp. 1-41.
Lu, C.J. and W.Q. Meeker (1993), "Using Degradation Measures to Estimate a Time-to-Failure Distribution," Technometrics, Vol 35, No 2, pp. 161-174.
Meeker, W.Q., L.A. Escobar, and C.J. Lu (1998), “Accelerated degradation tests: modeling and analysis,” Technometrics, vol. 40 (2) pp. 89-99.
Oja, M., J.K. Line, G. Krishnan, R.G. Tryon (2007), “Electronic Prognostics with Analytical Models using Existing Measurands,” 61st Conference of the Society for Machinery Failure Prevention Technology (MFPT) 2007, Virginia Beach, April 17-19.
Pecht, M. and A. Dasgupta (1995), “Physics of Failure: An Approach to Reliable Product Development,” Journal of the Institute of Environmental Sciences 38 (5): 30 – 34.
Peel, L. (2008), “Data Driven Prognostics using a Kalman Filter Ensemble of Neural Network Models,” PHM '08, Denver CO, Oct 6-9.
Robinson, M.E. and M.T. Crowder (2000), "Bayesian Methods for a Growth-Curve Degradation Model with Repeated Measures," Lifetime Data Analysis, Vol 6, pp. 357-374.
Saxena, A., K. Goebel, D. Simon, N. Eklund (2008), "Prognostics Challenge Competition Summary: Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation," PHM '08, Denver CO, Oct 6-9.
Upadhyaya, B.R., M. Naghedolfeizi, and B. Raychaudhuri (1994), "Residual Life Estimation of Plant Components," P/PM Technology, June, pp. 22-29.
Valentin, R., M. Osterman, B. Newman (2003), “Remaining Life Assessment of Aging Electronics in Avionic Applications,” 2003 Proceedings of the Annual Reliability and Maintainability Symposium (RAMS): 313 – 318.
Wald, A. (1945), “Sequential Tests of Statistical Hypotheses,” The Annals of Mathematical Statistics June 1945, vol. 16 (2) pp. 117-186.
Wang, P. and D.W. Coit (2004), "Reliability Prediction based on Degradation Modeling for Systems with Multiple Degradation Measures," Proc. of the 2004 Reliability and Maintainability Symposium, pp. 302-307.
Wang,T. J. Yu, D. Seigel, and J. Lee (2008), “A Similarity-Based Prognostics Approach for Remaining Useful Life Estimation of Engineered Systems,” PHM '08, Denver CO, Oct 6-9.
Carlin, B.P and T.A. Louis (2000), Bayes and Empirical Bayes Methods for Data Analysis, 2nd ed. Boca Raton: Chapman and Hall/CRC.
Chinnam, R.B. (1999), "On-line Reliability Estimation of Individual Components, Using Degradation Signals", IEEE Transactions on Reliability, Vol 48, No 4, pp. 403-412.
Garvey, D. and J.W. Hines (2006), “Robust Distance Measures for On-Line Monitoring: Why Use Euclidean?” 7th International Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS) Conference on Applied Artificial Intelligence.
Garvey, J., R. Seibert, D. Garvey, and J.W. Hines (2007), "Application of On-Line Monitoring Techniques to Nuclear Plant Data," Nuclear Engineering and Technology, Vol. 39 No. 2.
Gelman, A., J. Carlin, H. Stern, and D. Rubin (2004), Bayesian Data Analysis 2nd ed. Boca Raton: Chapman and Hall/CRC.
Girish, T., S.W. Lam, J.S.R. Jayaram (2003), "Reliability Prediction Using Degradation Data – A Preliminary Study Using Neural Network-based Approach," Proc. European Safety and Reliability Conference (ESREL 2003), Maastricht, The Netherlands, Jun. 15-18.
Heimes, F.O. (2008), “Recurrent Neural Networks for Remaining Useful Life Estimation,” PHM '08, Denver CO, Oct 6-9.
Hines, J.W., D. Garvey, R. Seibert, A. Usynin, and S.A. Arndt, (2008) “Technical Review of On-Line Monitoring Techniques for Performance Assessment, Volume 2: Theoretical Issues,” NUREG/CR-6895 Vol 2.
Hines, J.W., J. Garvey, J. Preston, and A. Usynin (2007), “Empirical Methods for Process and Equipment Prognostics,” Reliability and Maintainability Symposium RAMS.
Koppen, M. (2004), “No-Free-Lunch Theorems and the Diversity of Algorithms”, Congress on Evolutionary Computation, 1, pp 235 – 241.
Kothamasu, R., S.H. Huang, and W.H. VerDuin (2006), “System Health Monitoring and Prognostics – A Review of Current Paradigms and Practices,” International Journal of Advanced Manufacturing Technology 28: 1012 – 1024.
Lindely, D.V. and A.F. Smith (1972), "Bayes Estimates for Linear Models," Journal of the Royal Statistical Society (B), Vol 34, No 1, pp. 1-41.
Lu, C.J. and W.Q. Meeker (1993), "Using Degradation Measures to Estimate a Time-to-Failure Distribution," Technometrics, Vol 35, No 2, pp. 161-174.
Meeker, W.Q., L.A. Escobar, and C.J. Lu (1998), “Accelerated degradation tests: modeling and analysis,” Technometrics, vol. 40 (2) pp. 89-99.
Oja, M., J.K. Line, G. Krishnan, R.G. Tryon (2007), “Electronic Prognostics with Analytical Models using Existing Measurands,” 61st Conference of the Society for Machinery Failure Prevention Technology (MFPT) 2007, Virginia Beach, April 17-19.
Pecht, M. and A. Dasgupta (1995), “Physics of Failure: An Approach to Reliable Product Development,” Journal of the Institute of Environmental Sciences 38 (5): 30 – 34.
Peel, L. (2008), “Data Driven Prognostics using a Kalman Filter Ensemble of Neural Network Models,” PHM '08, Denver CO, Oct 6-9.
Robinson, M.E. and M.T. Crowder (2000), "Bayesian Methods for a Growth-Curve Degradation Model with Repeated Measures," Lifetime Data Analysis, Vol 6, pp. 357-374.
Saxena, A., K. Goebel, D. Simon, N. Eklund (2008), "Prognostics Challenge Competition Summary: Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation," PHM '08, Denver CO, Oct 6-9.
Upadhyaya, B.R., M. Naghedolfeizi, and B. Raychaudhuri (1994), "Residual Life Estimation of Plant Components," P/PM Technology, June, pp. 22-29.
Valentin, R., M. Osterman, B. Newman (2003), “Remaining Life Assessment of Aging Electronics in Avionic Applications,” 2003 Proceedings of the Annual Reliability and Maintainability Symposium (RAMS): 313 – 318.
Wald, A. (1945), “Sequential Tests of Statistical Hypotheses,” The Annals of Mathematical Statistics June 1945, vol. 16 (2) pp. 117-186.
Wang, P. and D.W. Coit (2004), "Reliability Prediction based on Degradation Modeling for Systems with Multiple Degradation Measures," Proc. of the 2004 Reliability and Maintainability Symposium, pp. 302-307.
Wang,T. J. Yu, D. Seigel, and J. Lee (2008), “A Similarity-Based Prognostics Approach for Remaining Useful Life Estimation of Engineered Systems,” PHM '08, Denver CO, Oct 6-9.
Section
Technical Papers