A discrete hidden Markov model fault diagnosis strategy based on K-means clustering dedicated to PEM fuel cell systems of tramways |
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Authors: | Jiawei Liu Qi Li Weirong Chen Taiqiang Cao |
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Affiliation: | 1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, Sichuan Province, China;2. School of Engineering and Electric Information, Xihua University, Chengdu 610039, Sichuan Province, China |
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Abstract: | To solve the fault classification problems of fuel cell (FC) various health states for tramways, a discrete hidden Markov model (DHMM) fault diagnosis strategy based on K-means clustering is proposed. In this paper, the K-means clustering algorithm is used to filter the sample points which aren't consistent with the actual class labels. The Lloyd algorithm is employed to quantify the sample vector sets and obtain the discrete code combination of training samples and test samples. The Baum-Welch algorithm and forward-backward algorithm are respectively presented to train and deduce the DHMM. The classification results show that the six concerned faults can be detected and isolated. The targeted fault types include low air pressure, deionized glycol high inlet temperature, deionized humidification pump low pressure, deionized glycol outlet temperature signal voltage overrange, normal state and hydrogen leakage. The fault recognition rates with the novel approach are at best 94.17%. |
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Keywords: | Fuel cell tramways Fault diagnosis Discrete hidden Markov model K-means clustering Scalar quantization |
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