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.