A particle filtering framework for failure prognosis

Wu, B.; Vachtsevanos G.; Orchard M.

Keywords: model, models, state, density, learning, components, failure, algorithms, noise, cracks, estimation, probability, robustness, analysis, function, dynamic, filtering, techniques, bayesian, mathematical, systems), (control, (electronic), Gaussian, Kalman, Failing

Abstract

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. Copyright © 2005 by ASME.

Más información

Título de la Revista: 1604-2004: SUPERNOVAE AS COSMOLOGICAL LIGHTHOUSES
Editorial: ASTRONOMICAL SOC PACIFIC
Fecha de publicación: 2005
Página de inicio: 883
Página final: 884
URL: http://www.scopus.com/inward/record.url?eid=2-s2.0-33144484072&partnerID=q2rCbXpz