Online Fault Diagnosis for Open-cathode PEMFC Systems Based on Output Voltage Measurements and Data-driven Method

Fault diagnosis is essential for the stable and efficient operation of the proton exchange membrane fuel cell (PEMFC) system. However, the manifold balance of plant (BOP) components and the coupling phenomenon involving multiple physical fields will significantly increase the probability of system fault, which makes it difficult to realize a timely and effective diagnosis. In this study, a novel online diagnosis method for an open-cathode PEMFC system is proposed which is only based on output voltage measurements, and both the normal state and fault states caused by abnormal BOP components operation are taken into consideration. Specifically, the fault identification is realized based on the fusion of t-Distributed Stochastic Neighbor Embedding (t-SNE) and eXtreme Gradient Boosting (XGBoost), where the t-SNE is adopted to extract the diagnostic features from the individual cell output voltages and the XGBoost is adopted to identify the fault type based on the extracted diagnostic features. A facile method based on Grey Relational Analysis (GRA) is also proposed to quantify the fault degree, which can contribute to the condition-based maintenance of the system. The results validated by the fuel cell system diagnostic experiments reveal that the novel method can effectively identify the five health states and the fault degree. The overall accuracy is 99.31% and the diagnosis period is shortened from 0.3375s to 0.1416s after processing by t-SNE, which indicates that the proposed method can have a better performance compared with the traditional methods.