Anomaly detection via feature-aided tracking and hidden Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Dynamic multiple fault diagnosis: mathematical formulations and solution techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Dynamic multiple-fault diagnosis with imperfect tests
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Accuracy and robustness of decision making techniques in condition based maintenance
Journal of Intelligent Manufacturing
Data reconciliation in a smart home sensor network
Expert Systems with Applications: An International Journal
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We present a hidden Markov model (HMM) based algorithm for fault diagnosis in systems with partial and imperfect tests. The HMM-based algorithm finds the most likely state evolution, given a sequence of uncertain test outcomes over time. We also present a method to estimate online the HMM parameters, namely, the state transition probabilities, the instantaneous probabilities of test outcomes given the system state and the initial state distribution, that are fundamental to HMM-based adaptive fault diagnosis. The efficacy of the parameter estimation method is demonstrated by comparing the diagnostic accuracies of an algorithm with complete knowledge of HMM parameters with those of an adaptive one. In addition, the advantages of using the HMM approach over a Hamming-distance based fault diagnosis technique are quantified. Tradeoffs in computational complexity versus performance of the diagnostic algorithm are also discussed