Identifying Optimal Prognostic Parameters from Data: A Genetic Algorithms Approach
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. Traditional individual- based prognostics involve identifying an appropriate degradation measure to characterize the system's progression to failure. These degradation measures may be sensed measurements, such as temperature or vibration level, or inferred measurements, such as model residuals or physics-based model predictions using other sensed measurements. Often, it is beneficial to combine several measures of degradation to develop a single parameter, called a prognostic parameter. A parametric model is fit to this parameter and then extrapolated to some predefined critical failure threshold to estimate the system's remaining useful life. Commonly, identification of a prognostic parameter is accomplished through visual inspection of the available information and engineering judgment. However, a set of metrics to characterize the suitability of prognostic parameters has been proposed. These metrics include monotonicity, prognosability, and trendability. Monotonicity characterizes a parameter's general increasing or decreasing nature. Prognosability measures the spread of the parameter's failure value for a population of systems. Finally, trendability indicates whether the parameters for a population of systems have the same underlying trend,and hence can be described by the same parametric function. This research formalizes these metrics in a way that is robust to the noise found in real world systems. The metrics are used in conjunction with a Genetic Algorithms optimization routine to identify an optimal prognostic parameter for the Prognostics and Health Management (PHM) Challenge data from the 2008 PHM conference.
How to Cite
feature extraction, machine learning
(Coble and Hines, 2009) Coble, J. and J.W. Hines, "Fusing Data Sources for Optimal Prognostic Parameter Selection." Sixth American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies NPIC&HMIT 2009, Knoxville, Tennessee, April 5-9, 2009.
(Gelman et al, 2004) Gelman, A., J. Carlin, H. Stern, and D. Rubin, Bayesian Data Analysis 2nd ed. Boca Raton: Chapman and Hall/CRC: 2004.
(Girish et al, 2003) Girish, T., S.W. Lam, J.S.R. Jayaram, "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, 2003.
(Haupt and Haupt, 2004) Haupt and Haupt. Practical Genetic Algorithms, John Wiley & Sons Ltd, Hoboken, New Jersey: 2004.
(Hines et al, 2006) Hines, J.W., R. Seibert, S.A. Arndt, "Technical Review of On-Line Monitoring Techniques for Performance Assessment (NUREG/CR-6895) V ol. 1, State-of-the-Art." Published January, 2006.
(Hines et al, 2007) Hines, J.W., J. Garvey, J. Preston, and A. Usynin, “Empirical Methods for Process and Equipment Prognostics,” Reliability and Maintainability Symposium RAMS, 2007.
(Lindely and Smith, 1972) Lindely, D.V. and A.F. Smith, "Bayes Estimates for Linear Models," Journal of the Royal Statistical Society (B), Vol 34, No 1, 1972, pp. 1-41.
(Lu and Meeker, 1993) Lu, C.J. and W.Q. Meeker, "Using Degradation Measures to Estimate a Time-to- Failure Distribution," Technometrics, Vol 35, No 2, May 1993, pp. 161-174.
(Meeker et al, 1998) Meeker, W.Q., L.A. Escobar, and C.J. Lu, “Accelerated degradation tests: modeling and analysis,” Technometrics, vol. 40, no. 2, pp. 89-99, 1998.
(Robinson and Crowder, 2000) Robinson, M.E. and M.T. Crowder, "Bayesian Methods for a Growth- Curve Degradation Model with Repeated Measures," Lifetime Data Analysis, Vol 6, 2000, pp. 357-374.
(Saxena et al, 2008) Saxena, A., K. Goebel, D. Simon, N. Eklund, "Prognostics Challenge Competition Summary: Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation," PHM '08, Denver CO, Oct 6-9, 2008.
(Upadhyaya et al, 1994) Upadhyaya, B.R., M. Naghedolfeizi, and B. Raychaudhuri, "Residual Life Estimation of Plant Components," P/PM Technology, June 1994, pp. 22-29.
(Wald, 1945) Wald, A. "Sequential Tests of Statistical Hypotheses." Annals of Mathematical Statistics 16 (2): 117–186.
(Wang and Coit, 2004) Wang, P. and D.W. Coit, "Reliability Prediction based on Degradation Modeling for Systems with Multiple Degradation Measures," Proc. of the 2004 Reliability and Maintainability Symposium, 2004, pp. 302-307.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.