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HMM based modeling and health condition assessment for degradation process

机译：基于肝化过程的嗯建模与健康状况评估

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The modeling and health condition assessment for degradation process are crucial to the effective machine fault diagnosis and prognosis. They provide a potent tool for operators in decision-making by specifying the present machine state and estimating the remaining useful life (RUL). In this paper, the health conditions of degradation process are modeled as a hidden Markov chain and the physical outputs are modeled as the stochastic events whose probability depends on the Markov chain state. The expectation maximization (E-M) algorithm is proposed to learn parameters of the modeled hidden Markov model (HMM) and the iteration convergence is demonstrated. A maximum a posteriori (MAP) current health condition assessment approach is also proposed.
机译：降解过程的建模和健康状况评估对于有效机器故障诊断和预后至关重要。它们通过指定本机状态并估算剩余的使用寿命（RUL），为运营商提供有效的工具。在本文中，降解过程的健康状况被建模为隐藏的马尔可夫链，物理输出被建模为随机事件，其概率取决于马尔可夫链状态。提出了期望最大化（E-M）算法学习建模隐藏马尔可夫模型（HMM）的参数，并说明迭代收敛。还提出了最大的后验（地图）当前健康状况评估方法。

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