Fault Diagnostics of Helicopter Gearboxes Based on Multi-Sensor Mixtured Hidden Markov Models
Fault Diagnostics of Helicopter Gearboxes Based on Multi-Sensor Mixtured Hidden Markov Models
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DOI：
10.1115/1.4005830
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Zhongsheng Chen;Yongmin Yang
中科院分区：
文献类型：
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作者：
Zhongsheng Chen;Yongmin Yang
齿轮箱故障的准确识别对直升机的安全运行至关重要。虽然具有高斯观测值的隐马尔可夫模型（HMM）已成功地用于机械系统的故障诊断，高斯HMM必须假设观测序列是从高斯过程生成的。相反，来自直升机齿轮箱的振动信号通常是非高斯和非平稳的。在实际应用中，往往需要使用多个传感器进行更准确的故障诊断。因此，经典的高斯隐马尔可夫模型可能无法满足直升机齿轮箱的需要，需要研究新的隐马尔可夫模型来建模多传感器，非高斯信号。提出了一种基于多传感器信号的多传感器混合隐马尔可夫模型（MSMHMM）。对于MSMHMM，每个传感器信号将被视为非高斯源的混合，因此它可以很好地描述非高斯观测序列。然后，详细阐述了基于期望最大化（EM）算法的MSMHMM参数学习机制，提出了基于MSMHMM的故障诊断框架。最后，以某型直升机齿轮箱为例，对所提出的方法进行了验证。
Accurate identification of faults in gearboxes is of vital importance for the safe operation of helicopters. Although hidden Markov models (HMMs) with Gaussian observations have been successfully used for fault diagnostics of mechanical systems, a Gaussian HMM must assume that the observation sequence is generated from a Gaussian process. Conversely, vibration signals from helicopter gearboxes are often non-Gaussian and non-stationary. Also, it always needs to use multi-sensors for more accurate fault diagnostics in practice. Thus, a classical Gaussian HMM may not meet the need of helicopter gearboxes, and it needs to study novel HMMs to model multi-sensor, non-Gaussian signals. This paper presents a multi-sensor mixtured HMM (MSMHMM), which is built on multi-sensor signals. For a MSMHMM, each sensor signal will be considered as the mixture of non-Gaussian sources, so it can depict non-Gaussian observation sequences very well. Then, learning mechanisms of MSMHMM parameters are formulated in detail based on the expectation-maximization (EM) algorithm and a framework of MSMHMM-based fault diagnostics is proposed. In the end, the proposed method is validated on a helicopter gearbox, and the results are very exciting.