Bayesian estimation techniques are finding application domains in machinery fault diagnosis and prognosis of the remaining useful life of a failing component/subsystem. This paper introduces a methodology for accurate and precise prediction of a failing component based on particle filtering and learning strategies. This novel approach employs a state dynamic model and a measurement model to predict the posterior probability density function of the state, i.e., to predict the time evolution of a fault or fatigue damage. It avoids the linearity and Gaussian noise assumption of Kalman filtering and provides a robust framework for long-term prognosis while accounting effectively for uncertainties. Correction terms are estimated in a learning paradigm to improve the accuracy and precision of the algorithm for long-term prediction. The proposed approach is applied to a crack fault and the results support its robustness and superiority.
A Particle Filtering Framework for Failure Prognosis
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Orchard, M, Wu, B, & Vachtsevanos, G. "A Particle Filtering Framework for Failure Prognosis." Proceedings of the World Tribology Congress III. World Tribology Congress III, Volume 2. Washington, D.C., USA. September 12–16, 2005. pp. 883-884. ASME. https://doi.org/10.1115/WTC2005-64005
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