Evaluating and Optimizing Analytic Signals



Published Nov 24, 2021
Shashvat Prakash Antoni Brzoska


Condition-based maintenance is becoming a viable option for mitigating the high cost of unscheduled repairs. However, as data-driven approaches gain favor, there is a need to preserve the underlying physical degradation models in order to reasonably justify preventative maintenance. One solution is a class of models which augment physics with data-driven heuristics. The nature of the underlying degradation is explained with physics while detectability and decision nuances can be overcome with statistics and signal processing.

This paper describes a process for evaluating analytical models and using this evaluation for improving overall detection. The method involves optimizing a tunable filter to process signals such that the precursor signature preceding failure events approximates a known degradation behavior.

How to Cite

Prakash, S., & Brzoska, A. (2021). Evaluating and Optimizing Analytic Signals. Annual Conference of the PHM Society, 13(1). https://doi.org/10.36001/phmconf.2021.v13i1.2968
Abstract 4332 | PDF Downloads 1616



Evaluation, Signal Processing, Classifier

[2] L. Zhang, X. Li, J. Yu, “A Review of Fault Prognostics in Condition Based Maintenance,” Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology (2006).
[3] J. H. Luo, M. Namburu, K. Pattipati, Q. Liu, M. Kawamoto, S. Chigusa, "Model-based prognostic techniques [maintenance applications]," Proceedings of AUTOTESTCON 2003, IEEE Systems Readiness Technology Conference. 22-25, 330-340(2003).
[4] V. Fornlof, D. Galar, A. Syberfeldt and T. Almgren, “Maintenance, Prognostics and Diagnostic Approaches for Aircraft engines,” IEEE Metrology for Aerospace (2016).
[5] J.P. Sprong, X. Jiang, and H. Polinder, Deployment of Prognostics to Optimize Aircraft Maintenance - A Literature Review,” Proceedings of the Annual Conference of the Prognostics and Health Management Society (2019).
[6] K. Pipe, “Practical Prognostics for Condition Based Maintenance,” International Conference on Prognostics and Health Management (2008).
[7] T. Fawcett, “An Introduction to ROC Analysis,” Pattern Recognition Letters. 27 (8): 861–874 (2006).
[8] R. F. Estrada and E. A. Starr, “50 Years of Acoustical Signal Processing for Detection: Coping with the Digital Revolution,” IEEE Annals of the History of Computing 65-78, (2005).
[9] R. B. Abernethy, J. E. Breneman, C. H. Medlin, G. L. Reinman. Weibull Analysis Handbook. West Palm Beach, Pratt and Whitney Government Products Division, Nov. 1983.
[10] S. Pattabhiraman, C. Gogu, N. Kim, R. T. Haftka, and C. Bes, “Skipping unnecessary maintenance using an on-board structural health monitoring system,” Proc IMechE Part O: J Risk and Reliability 226(5) 549-560, (2012).
[11] X. Lei, and P. A. Sandborn. "PHM-based wind turbine maintenance optimization using real options." Int J Progn Health Manag 7.1 (2016): 1-14
[12] X. Lei, P. A. Sandborn, “Maintenance Scheduling Based on Remaining Useful Life Predictions for Wind Farms Managed Using Power Purchase Agreements,” Renewable Energy, vol. 116, Part B, pp.188-198 (2018).
[13] Z.Tian, D. Lin, and B. Wu, “Condition based maintenance optimization considering multiple objectives”
[xx] W. H. J. M. Geudens, P. J. M. Sonnemans, V. T. Petkova and A. C. Brombacher, "Soft reliability, a new class of problems for innovative products: "how to approach them"," Proceedings of Annual Reliability and Maintainability Symposium, 374-378 (2005).
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