Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis
Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis
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DOI：
10.1016/j.ejor.2006.01.041
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发表时间：
2007-05-01
影响因子：
6.4
通讯作者：
He, David
中科院分区：
文献类型：
--
作者：
Dong, Ming;He, David
本文提出了一个用于多传感器设备诊断和预测的集成平台。该集成框架基于隐式半马尔可夫模型（HSMM）。与标准隐马尔可夫模型 (HMM) 中的状态不同，HSMM 中的状态会生成一段观测值，而不是 HMM 中的单个观测值。因此，与 HMM 相比，HSMM 结构具有时间成分。在此框架中，HSMM 的状态用于表示组件的健康状态。健康状态的持续时间由显式高斯概率函数建模。模型参数（即初始状态分布、状态转移概率矩阵、观察概率矩阵和健康状态持续时间概率分布）通过改进的前向-后向训练算法进行估计。推导了模型参数的重估计公式。经过训练的 HSMM 可用于诊断组件的健康状态。通过健康状态持续时间概率分布的参数估计和所提出的后向递归方程，我们可以预测组件的剩余有用寿命。为了确定每个传感器信息的“值”，采用判别函数分析来调整分配给传感器的权重或重要性。因此，在这个基于 HSMM 的框架中，传感器融合成为可能。所提出的框架和方法的验证在现实世界的应用中进行：监测卡特彼勒公司的液压泵。结果表明，正确诊断率的提高确实非常有前景。此外，设备预测可以在同一集成框架中实现。 (c) 2006 Elsevier B.V. 保留所有权利。
This paper presents an integrated platform for multi-sensor equipment diagnosis and prognosis. This integrated framework is based on hidden semi-Markov model (HSMM). Unlike a state in a standard hidden Markov model (HMM), a state in an HSMM generates a segment of observations, as opposed to a single observation in the HMM. Therefore, HSMM structure has a temporal component compared to HMM. In this framework, states of HSMMs are used to represent the health status of a component. The duration of a health state is modeled by an explicit Gaussian probability function. The model parameters (i.e., initial state distribution, state transition probability matrix, observation probability matrix, and health-state duration probability distribution) are estimated through a modified forward-backward training algorithm. The re-estimation formulae for model parameters are derived. The trained HSMMs can be used to diagnose the health status of a component. Through parameter estimation of the health-state duration probability distribution and the proposed backward recursive equations, one can predict the useful remaining life of the component. To determine the "value" of each sensor information, discriminant function analysis is employed to adjust the weight or importance assigned to a sensor. Therefore, sensor fusion becomes possible in this HSMM based framework.The validation of the proposed framework and methodology are carried out in real world applications: monitoring hydraulic pumps from Caterpillar Inc. The results show that the increase of correct diagnostic rate is indeed very promising. Furthermore, the equipment prognosis can be implemented in the same integrated framework. (c) 2006 Elsevier B.V. All rights reserved.