A Particle Filtering Based Framework For On Line Fault Diagnosis And Failure Prognosis
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A Particle Filtering-based Framework for On-line Fault Diagnosis and Failure Prognosis
Author | : Marcos Eduardo Orchard |
Publisher | : |
Total Pages | : 123 |
Release | : 2007 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
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This thesis presents an on-line particle-filtering-based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology assumes the definition of a set of fault indicators, which are appropriate for monitoring purposes, the availability of real-time process measurements, and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. The incorporation of particle-filtering (PF) techniques in the proposed scheme not only allows for the implementation of real time algorithms, but also provides a solid theoretical framework to handle the problem of fault detection and isolation (FDI), fault identification, and failure prognosis. Founded on the concept of sequential importance sampling (SIS) and Bayesian theory, PF approximates the conditional state probability distribution by a swarm of points called particles and a set of weights representing discrete probability masses. Particles can be easily generated and recursively updated in real time, given a nonlinear process dynamic model and a measurement model that relates the states of the system with the observed fault indicators.
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