Fault Diagnosis for PEMFC Water Management Subsystem Based on Learning Vector Quantization Neural Network and Kernel Principal Component Analysis

Abstract

To solve the problem of water management subsystem fault diagnosis in a proton exchange membrane fuel cell (PEMFC) system, a novel approach based on learning vector quantization neural network (LVQNN) and kernel principal component analysis (KPCA) is proposed. In the proposed approach, the KPCA method is used for processing strongly coupled fault data with a high dimension to reduce the data dimension and to extract new low-dimensional fault feature data. The LVQNN method is used to carry out fault recognition using the fault feature data. The effectiveness of the proposed fault detection method is validated using the experimental data of the PEMFC power system. Results show that the proposed method can quickly and accurately diagnose the three health states: normal state, water flooding failure and membrane dry failure, and the recognition accuracy can reach 96.93%. Therefore, the method proposed in this paper is suitable for processing the fault data with a high dimension and abundant quantities, and provides a reference for the application of water management subsystem fault diagnosis of PEMFC.

A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues

“…Nevertheless, both simulation and experimental results showed the good accuracy of the proposed technique. Similar works can be found in [163][164][165].…”

“…Symptoms Consequences Diagnostics Recovering mechanism [104] operation at nominal power [112] low temperature, and poor air distribution [52] water droplets retained at the GDL [76] anode flooding (unoptimized exhaustion system) [112] excess of water at the anode [113] increased pressure drops [54,118] increased membrane resistance [156] high level of overall impedance [159] double layer effect affected [101] temperature decreasing rapidly (oscillating dewpoint) + increased cathode pressure [76] voltage degradation [169] internal humidity levels higher than 100% [169] high reactants pressure [174] reactants hygrometry higher than 1.1 [125] low air stoichiometry [100] decrease in electric power [118] increased imaginary and real part in EIS results-Nyquist plot-(cathode flooded) [118] decrease temperature (bigger EIS semi-circle diameter) [52,53] neutron imaging [101] online machine learning: ENN (cathode pressure residuals) [112] infrared spectroscopy [113] pressure, mass flow rate and humidity monitorization [114] anode to cathode pressure drop [115][116][117] EIS [118] empirical equivalent model parameter estimation [156] harmonic impedance measurement [159] online threshold around the nominal polarization curve (current interrupt method) [160] online signal based (EMD) [161] online machine learning: BN [163][164][165] online machine learning algorithm…”