Generalized Spatial-Temporal Fault Location Method for Solid Oxide Fuel Cells Using LSTM and Causal Inference

• Published in: IEEE transactions on transportation electrification 2022-12, Vol.8 (4), p.4583-4594
• Main Authors: Peng, Jingxuan, Huang, Jian, Jiang, Chang, Xu, Yuan-Wu, Wu, Xiao-Long, Li, Xi
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Causal inference; Data models; Degradation; Fault location; Fuel cells; generalized fault spatiotemporal locating method; Inference; long short-term memory (LSTM); migration properties; Numerical models; Optimization; Predictive models; solid oxide fuel cell (SOFC); Solid oxide fuel cells
• ISSN: 2332-7782; 2577-4212; 2332-7782
• DOI: 10.1109/TTE.2022.3187870

An effective fault locating method is necessary to ensure the stable and efficient operation of solid oxide fuel cells (SOFCs). There is still a lack of a common fault locating method for locating multiple faults in SOFC systems. Therefore, this article proposes a multifault spatiotemporal locating method combining long short-term memory (LSTM) artificial neural network and causal inference. This method does not rely on the SOFC mechanism model and does not require a large amount of fault data. This method has good migratory characteristics and can be used with different systems. This method first reconstructs the experimental data by LSTM and locates the fault occurrence time according to the reconstruction error. Then, the space where the fault occurred is located by the causal inference method. At the same time, multiple locating methods are compared. Finally, a performance optimization method is adopted from the system level to improve the efficiency of the system. From the comparison results, it can be seen that the scheme proposed in this article is able to locate different faults in time and space with an accuracy of 92.6%. In addition, the system efficiency can be improved by 18.7% after the corresponding optimization methods are adopted.