Real-time data-driven fault diagnosis of proton exchange membrane fuel cell system based on binary encoding convolutional neural network

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The performance of proton exchange Membrane fuel cell (PEMFC) fault diagnosis system plays an important role in normal operation of PEMFC. Therefore, a new fault diagnosis algorithm based on binary matrix encoding neural network called BinE-CNN is proposed. In BinE-CNN, high-dimensional features are extracted through binary encoding, and the feature maps are transferred to a convolutional neural network (CNN) to realize seven-category fault classification. For development of BinE-CNN, a PEMFC model is modeled to generate simulative datasets. Simulative test precision and Frames per second (FPS) of BinE-CNN have reached respectively 0.973 and 999.8 (better than support vector machines (SVM), long short-term memory neural network (LSTM), etc.). In experimental verification section, fault datasets are collected during bench test. After that, BinE-CNN is deployed on vehicle control unit (VCU) to verify its engineering value (real-time and precision). The result meet both requirements, with time cost of 96.15?ms and precision of 0.931.

» Author: Su Zhou, Yanda Lu, Datong Bao, Keyong Wang, Jing Shan, Zhongjun Hou

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