Fast Fault Diagnosis Method of Evaporatively Cooled Fuel Cell System Based on Online Sequential Extreme Learning Machine and Principal Component Analysis
Liu Jiawei, Li Qi, Chen Weirong, Yu Jiaxi, Yan Yu
School of Electrical Engineering Southwest Jiaotong University Chengdu 611756 China
Abstract：In order to solve the fault diagnosis problem of evaporatively cooled fuel cell system, a novel fast fault diagnosis method for evaporatively cooled fuel cell system was proposed based on online sequential extreme learning machine and principal component analysis. The principal component analysis was used to filter the redundant information and obtain the fault eigenvectors that could reflect the states of evaporatively cooled fuel cell system. The fault feature vectors were classified by online sequential extreme learning machine, thereby effectively improving the diagnostic accuracy of the model and reducing the computation time. The case analysis shows that the novel method can quickly identify three healthy states of membrane drying failure, hydrogen leakage failure, and normal state. The diagnostic accuracy of the algorithm is 99.67% and the computation time is 0.296 9 seconds. The diagnostic accuracy of the novel method is 14.34% and 9.34% higher than those of SVM and BPNN respectively, and the computation time of online sequential extreme learning machine is only 1/1 011 of SVM and 1/132 of BPNN. Therefore, the proposed method is suitable for online fault diagnosis of evaporatively cooled fuel cell system with large data samples and multi-data dimensions.
刘嘉蔚, 李奇, 陈维荣, 余嘉熹, 燕雨. 基于在线序列超限学习机和主成分分析的蒸汽冷却型燃料电池系统快速故障诊断方法[J]. 电工技术学报, 2019, 34(18): 3949-3960.
Liu Jiawei, Li Qi, Chen Weirong, Yu Jiaxi, Yan Yu. Fast Fault Diagnosis Method of Evaporatively Cooled Fuel Cell System Based on Online Sequential Extreme Learning Machine and Principal Component Analysis. Transactions of China Electrotechnical Society, 2019, 34(18): 3949-3960.
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