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A particle-filtering approach for on-line fault diagnosis and failure prognosis

School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0250, USA, Electrical Engineering Department, University of Chile, Av. Tupper 2007, Santiago, Chile, marcos.orchard{at}gmail.com

George J. Vachtsevanos

School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0250, USA

This paper introduces an on-line particle-filtering (PF)-based framework for fault diagnosis and failure prognosis in non-linear, non-Gaussian systems. This framework considers the implementation of two autonomous modules. A fault detection and identification (FDI) module uses a hybrid state-space model of the plant and a PF algorithm to estimate the state probability density function (pdf) of the system and calculates the probability of a fault condition in real-time. Once the anomalous condition is detected, the available state pdf estimates are used as initial conditions in prognostic routines. The failure prognostic module, on the other hand, predicts the evolution in time of the fault indicator and computes the pdf of the remaining useful life (RUL) of the faulty subsystem, using a non-linear state-space model (with unknown time-varying parameters) and a PF algorithm that updates the current state estimate. The outcome of the prognosis module provides information about the precision and accuracy of long-term predictions, RUL expectations and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary gear plate are used to validate the proposed approach.

Key Words: failure prognosis • fault detection • fault identification • particle filtering.

This version was published on June 1, 2009

Transactions of the Institute of Measurement and Control, Vol. 31, No. 3-4, 221-246 (2009)
DOI: 10.1177/0142331208092026


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