Optimal tuning of particle filtering random noise for monotonic degradation processes

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Published Jul 5, 2016
Matteo Corbetta Claudio Sbarufatti Marco Giglio

Abstract

Particle filtering is a model-based, Bayesian filtering algorithm widely employed in many scientific and engineering fields. It has been recently applied many times for the diagnosis and prognosis of engineering systems. Within the prognostics and health management scenario, particle filtering stood out as an effective and robust algorithm to predict the system’s remaining useful life because of its capability in tracking nonlinear/non-Gaussian systems. One of the fundamental equations underneath particle filtering is the evolution equation describing the system’s dynamics. This equation composes of a deterministic model and an artificiallyadded random process or random noise, thus making the evolution equation a stochastic equation. The selection of random
noise is up to the algorithm’s designer discretion and may vary on a case-by-case basis. Concentrating on the field of structural degradation processes, many studies on particle filtering-based prognostics have shown encouraging results. Though, they did not provide detailed discussions in supporting the appropriateness of the selected random noises altering the degradation model. An improper choice may cause the algorithm to be inefficient, moving the projected trajectories outside the state-space domain of the system, sometimes also introducing a bias in the stochastic evolution model. Therefore, this work examines the evolution equation with the aim of creating an optimal prognostic framework for monotonic degradation phenomena. The paper gives special emphasis to structural degradation caused by fatigue, which is a widespread monotonic damage progression process. Some of the existing works are reviewed, discussing the effectiveness of the
random noises embedded in the algorithm. Eventually, the paper presents an unbiased, optimal random noise for monotonic damage progression, pointing out the strengths of the proposed solution against formulations suggested in literature. The presented particle filtering-based prognostic algorithm is applied to experimental crack growth observations on an aeronautical stiffened structure and the prediction performance is validated using dedicated prognostic metrics.

How to Cite

Corbetta, M., Sbarufatti, C., & Giglio, M. (2016). Optimal tuning of particle filtering random noise for monotonic degradation processes. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1589
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Keywords

particle filtering, fatigue prognosis, structural health monitoring, remaining useful life prediction

References
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. Signal Processing, IEEE Transactions on, 50(2), 174–188.
Baraldi, P., Compare, M., Sauco, S., & Zio, E. (2013). Ensemble neural network-based particle filtering for prognostics. Mechanical Systems and Signal Processing, 41(1), 288–300.
Baraldi, P., Mangili, F., & Zio, E. (2012). A kalman filterbased ensemble approach with application to turbine creep prognostics. Reliability, IEEE Transactions on, 61(4), 966–977.
Cadini, F., Zio, E., & Avram, D. (2009a). Model-based monte carlo state estimation for condition-based component replacement. Reliability Engineering & System Safety, 94(3), 752–758.
Cadini, F., Zio, E., & Avram, D. (2009b). Monte carlo-based filtering for fatigue crack growth estimation. Probabilistic Engineering Mechanics, 24(3), 367–373.
Chiachıo, J., Chiachıo, M., Saxena, A., Rus, G., & Goebel, K. (2013). An energy-based prognostics framework to predict fatigue damage evolution in composites. In Proceedings of the annual conference of the prognostics and health management society (Vol. 1, pp. 363–371).
Chiachıo, M., Chiachıo, J., Saxena, A., Rus, G., & Goebel, K. (2014). An efficient simulation framework for prognostics of asymptotic processes-a case study in composite materials. In Proceedings of the european conference of the prognostics and health management society, nantes, france (pp. 202–214).
Corbetta, M., Saxena, A., Giglio, M., & Goebel, K. (2015). Evaluation of multiple damage-mode models for prognostics of carbon fiber-reinforced polymers. In International workshop on structural health monitoring.
Corbetta, M., Sbarufatti, C., Manes, A., & Giglio, M. (2014). On dynamic state-space models for fatigue-induced structural degradation. International Journal of Fatigue, 61, 202–219. doi:
http://dx.doi.org/10.1016/j.ijfatigue.2013.11.008
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential monte carlo sampling methods for bayesian filtering. Statistics and computing, 10(3), 197–208.
Gordon, N. J., Salmond, D. J., & Smith, A. F. (1993). Novel approach to nonlinear/non-gaussian bayesian state estimation. , 140(2), 107–113.
Haug, A. (2005). A tutorial on bayesian estimation and tracking techniques applicable to nonlinear and nongaussian processes. MITRE Corporation, McLean.
Jouin, M., Gouriveau, R., Hissel, D., Péra, M.-C., & Zerhouni, N. (2015). Particle filter-based prognostics: Review, discussion and perspectives. Mechanical Systems and Signal Processing.
Liu, J., & West, M. (2001). Combined parameter and state estimation in simulation-based filtering. In Sequential monte carlo methods in practice (pp. 197–223). Springer.
Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., & Vachtsevanos, G. (2008). Advances in uncertainty representation and management for particle filtering applied to prognostics. In Prognostics and health management, 2008. phm 2008. international conference on (pp. 1–6).
Orchard, M. E., & Vachtsevanos, G. J. (2007). A particle filtering-based framework for real-time fault diagnosis and failure prognosis in a turbine engine. In Control & automation, 2007. med’07. mediterranean conference on (pp. 1–6).
Orchard, M. E., & Vachtsevanos, G. J. (2009). A particlefiltering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control.
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, 52, 228–247.
Sankararaman, S., & Goebel, K. (2015). Uncertainty in prognostics and systems health management. International Journal of Prognostics and Health Management, 6.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. In Prognostics and health management, 2008. phm 2008. international conference on (pp. 1–17).
Sbarufatti, C., Manes, A., & Giglio, M. (2014). Application of sensor technologies for local and distributed structural health monitoring. Structural Control and Health Monitoring, 21(7), 1057–1083.
Tang, L., DeCastro, J., Kacprzynski, G., Goebel, K., & Vachtsevanos, G. (2010). Filtering and prediction techniques for model-based prognosis and uncertainty management. In Prognostics and health management conference, 2010. phm’10. (pp. 1–10).
Yang, W., Yuan, S., Qiu, L., Zhang, H., & Ling, B. (2012). A particle filter and lamb wave based on-line prognosis method of crack propagation in aluminum plates. In 4th international symposium on ndt in aerospace.
Zio, E., & Di Maio, F. (2012). Fatigue crack growth estimation by relevance vector machine. Expert Systems with Applications, 39(12), 10681–10692.
Zio, E., & Peloni, G. (2011). Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering & System Safety, 96(3), 403–409.
Section
Technical Papers