A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests
A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests
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DOI：
10.1109/5326.897073
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发表时间：
2000-11-01
期刊：
影响因子：
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通讯作者：
Patterson-Hine, A
中科院分区：
文献类型：
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作者：
Ying, J;Kirubarajan, T;Patterson-Hine, A
本文提出了一种基于隐马尔可夫模型（HMM）的故障诊断算法。基于HMM的算法结束最可能的状态演化，给定一系列不确定的测试结果随时间的推移。我们还提出了一种在线估计HMM参数的方法，即状态转移概率，给定系统状态和初始状态分布的测试结果的瞬时概率，这是基于HMM的自适应故障诊断的基础。通过比较具有HMM参数的完整知识的算法与自适应算法的诊断精度，证明了参数估计方法的有效性。此外，使用HMM方法的优势，基于汉明距离的故障诊断技术进行量化。权衡的计算复杂性与性能的诊断算法进行了讨论。
ln this paper, we present a hidden Markov model (HMM) based algorithm for fault diagnosis in systems with partial and imperfect tests. The HMM-based algorithm Ends 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 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.