Sequence Fault Diagnosis for PEMFC Water Management Subsystem Using Deep Learning With t-SNE

For solving the problem of sequence failure diagnosis of proton exchange membrane fuel cell (PEMFC) water management subsystem, this paper proposes a PEMFC failure diagnosis method of time series based on the bidirectional long short-term memory (BiLSTM) network and t-distributed stochastic neighbor embedding (t-SNE). This approach adopts the normalization strategy to eliminate the influence caused by dimensional differences of different parameters. The t-SNE is presented to decrease the dimensionality of normalized data to the estimate of intrinsic dimensionality to extract key characteristic variables. The width of the diagnostic window is set to transform the original single moment diagnosis problem into the fault diagnosis problem of multi-variable time series, which is more consistent with the time scale and physical evolution law of the PEMFC water management fault generation. The 672 sets of training sets and 448 sets of test sets are learned and tested by the BiLSTM. The experimental results show that the BiLSTM-tSNE method can realize the sequence fault diagnosis of the PEMFC water management subsystem with 96.88% diagnostic accuracy and 24 s of operation time. Compared with the conventional approach of multi-class support vector machine algorithm, the training accuracy and the testing accuracy of the proposed method are improved by 15% and 16.88%, respectively. The operation time of the presented approach is only about 1/28 of the multi-class support vector machine algorithm.