Data-driven Fault Diagnosis for PEM Fuel Cell System Using Sensor Pre-Selection Method and Artificial Neural Network Model - Archive ouverte HAL
Article Dans Une Revue IEEE Transactions on Energy Conversion Année : 2022

Data-driven Fault Diagnosis for PEM Fuel Cell System Using Sensor Pre-Selection Method and Artificial Neural Network Model

Résumé

Fault diagnosis is a critical process for the reliability and durability of proton exchange membrane fuel cells (PEMFCs). Due to the complexity of internal transport processes inside the PEMFCs, developing an accurate model considering various failure mechanisms is extremely difficult. In this paper, a novel data-driven approach based on sensor pre-selection and artificial neural network (ANN) are proposed. Firstly, the features of sensor data in time-domain and frequency-domain are extracted for sensitivity analysis. The sensors with poor response to the changes of system states are filtered out. Then experimental data monitored by the remaining sensors are utilized to establish the fault diagnosis model by using the ANN model. Levenberg-Marquardt (LM) algorithm, resilient propagation (RP) algorithm, and scaled conjugate gradient (SCG) algorithm are utilized in the training process, respectively. The results demonstrate that the diagnostic accuracy reaches 99.2% and the recall reaches 98.3%. The effectiveness of the proposed method is verified by comparing the diagnostic results in this work and that by support vector machine (SVM) and logistic regression (LR). Besides, the high computational efficiency of the proposed method supports the possibility of online diagnosis. Meanwhile, timely fault diagnosis can provide guidance for fault tolerant control of the PEMFCs system.
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Dates et versions

hal-03891109 , version 1 (09-12-2022)

Identifiants

  • HAL Id : hal-03891109 , version 1

Citer

Yanqiu Xing, Bowen Wang, Zhichao Gong, Zhongjun Hou, Fuqiang Xi, et al.. Data-driven Fault Diagnosis for PEM Fuel Cell System Using Sensor Pre-Selection Method and Artificial Neural Network Model. IEEE Transactions on Energy Conversion, 2022, 37 (3), pp.1589-1599. ⟨hal-03891109⟩
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