A hidden Markov model based algorithm for online fault diagnosis with partial and imperfect tests

In this paper, we present a Hidden Markov Model (HMM) based algorithm for online fault diagnosis in complex large-scale systems with partial and imperfect tests. The HMM-based algorithm handles test uncertainties and inaccuracies, finds the best estimate of system states and identifies the dynamic changes in system states, such as from a fault-free state to a faulty one. We also present two methods to estimate the model parameters, namely, the state transition probabilities and the instantaneous probabilities of observed test outcomes, for adaptive fault diagnosis. In order to validate the adaptive parameter estimation techniques, we present simulation results with and without the knowledge of HMM parameters. In addition, the advantages of using the HMM approach over a Hamming-distance based fault diagnosis technique are quantified. Tradeoffs in complexity versus performance of the diagnostic algorithm are discussed