Journal
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
Volume 43, Issue 27, Pages 12428-12441Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2018.04.163
Keywords
Fuel cell tramways; Fault diagnosis; Discrete hidden Markov model; K-means clustering; Scalar quantization
Categories
Funding
- National Natural Science Foundation of China [61473238]
- Scientific Funds for Outstanding Young Scientists of Sichuan Province of China [2015JQ0016]
- NEEC Open-end Fund of China [NEEC-2017-B01]
Ask authors/readers for more resources
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%. (C) 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available