A discrete hidden Markov model fault diagnosis strategy based on K-means clustering dedicated to PEM fuel cell systems of tramways

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%.