Proton Exchange Membrane Fuel Cell Fault Prediction Method Based on Deep Learning

Proton exchange membrane fuel cells (PEMFCs) have attracted significant attention in the fields of transportation, marine engineering, and aerospace due to their advantages of pollution-free operation, high efficiency, and low noise. However, reliability issues hinder their large-scale commercialization. To further enhance fuel cell reliability, this paper proposed a fault prediction method based on deep learning. First, for operational monitoring data including voltage, current, humidity, and temperature, feature parameters for fault diagnosis were selected based on fuel cell failure mechanisms. This approach reduces data dimensionality, suppresses redundant information, and improves the computational efficiency of the prediction model. Additionally, pre-processing techniques such as normalization and sliding time windows were employed to eliminate the effects of differing dimensions among monitoring parameters. Then, a fuel cell state prediction model based on the long short-term memory (LSTM) network was constructed. Its inputs were preprocessed multidimensional feature sequences, and its output predicts the fuel cell state for the next T time steps. Finally, the predicted state data was fed into a convolutional neural network (CNN)-based fault identification model to achieve fuel cell fault state prediction. The proposed method was validated using experimental fault data from fuel cell tests, and the results show that the model can predict failures in advance. By virtue of effective data preprocessing, future state prediction via LSTM, and fault recognition through CNN, this deep learning-based approach enables early prediction of operational anomalies in proton exchange membrane fuel cells.