Development of Online Fault Diagnosis Method for PEM Fuel Cell Based on Impedance at Optimal Frequency

2020 ◽  
Author(s):  
Tiancai Ma ◽  
Zhaoli Zhang ◽  
Weikang Lin ◽  
Jiajun Kang ◽  
Yanbo Yang
Keyword(s):  
Fuel Cell ◽  
Fault Diagnosis ◽  
Pem Fuel Cell ◽  
Optimal Frequency ◽  
Diagnosis Method

scholarly journals Overpotential evaluation of PEMFC using semi-empirical equation and SEM

2018 ◽  
Vol 67 ◽  
pp. 01015 ◽  
Author(s):  
Yutaro Akimoto ◽  
Shin-nosuke Suzuki
Keyword(s):  
Fuel Cells ◽  
Fuel Cell ◽  
Fault Diagnosis ◽  
Wind Power ◽  
Power Supply ◽  
Empirical Equation ◽  
Independent Power Supply ◽  
Diagnosis Method ◽  
Degradation Diagnosis ◽  
Semi Empirical

Fuel cells are a clean and weather-independent power supply. Solar and wind power are widespread in islands that are difficult to supply power. If problems are solved in the future, fuel cells are also expected to become popular. The widespread commercialization of PEMFC stacks depends on their reliability and fault diagnosis. In this study, we developed a degradation diagnosis method for the purpose of improving reliability. The output reduction of the fuel cell is separated into reduction factors called overpotentials. And the factor of the decrease is specified. In this paper, we show the proposed method and the degradation factors, and the effectiveness of the method.

scholarly journals An Early Fault Diagnosis Method of Rolling Bearings on the Basis of Adaptive Frequency Window and Sparse Coding Shrinkage

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 584
Author(s):  
Shuting Wan ◽  
Bo Peng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Sparse Coding ◽  
Optimal Frequency ◽  
Rolling Bearings ◽  
Diagnosis Method ◽  
Frequency Window ◽  
Envelope Spectrum ◽  
The Impact ◽  
Periodic Components

Early fault information of rolling bearings is weak and often submerged by background noise, easily leading to misdiagnosis or missed diagnosis. In order to solve this issue, the present paper puts forward a fault diagnosis method on the basis of adaptive frequency window (AFW) and sparse coding shrinkage (SCS). The proposed method is based on the idea of determining the resonance frequency band, extracting the narrowband signal, and envelope demodulating the extracted signal. Firstly, the paper introduces frequency window, which can slip on the frequency axis and extract the frequency band. Secondly, the double time domain feature entropy is proposed to evaluate the strength of periodic components in signal. The location of the optimal frequency window covering the resonance band caused by bearing fault is determined adaptively by this entropy index and the shifting/expanding frequency window. Thirdly, the signal corresponding to the optimal frequency window is reconstructed, and it is further filtered by the sparse coding shrinkage algorithm to highlight the impact feature and reduce the residue noise. Fourthly, the de-noised signal is demodulated by envelope operation, and the corresponding envelope spectrum is calculated. Finally, the bearing failure type can be judged by comparing the frequency corresponding to the spectral lines with larger amplitude in the envelope spectrum and the fault characteristic frequency. Two bearing vibration signals are applied to validate the proposed method. The analysis results illustrate that this method can extract more failure information and highlight the early failure feature. The data files of Case Western Reserve University for different operation conditions are used, and the proposed approach achieves a diagnostic success rate of 83.3%, superior to that of the AFW method, SCS method, and Fast Kurtogram method. The method presented in this paper can be used as a supplement to the early fault diagnosis method of rolling bearings.

scholarly journals Brain-inspired computational paradigm dedicated to fault diagnosis of PEM fuel cell stack

2017 ◽  
Vol 42 (8) ◽  
pp. 5410-5425 ◽  
Author(s):  
Zhixue Zheng ◽  
Simon Morando ◽  
Marie-Cécile Pera ◽  
Daniel Hissel ◽  
Laurent Larger ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Fault Diagnosis ◽  
Pem Fuel Cell ◽  
Fuel Cell Stack ◽  
Computational Paradigm

scholarly journals Fuzzy Logic Based Interactive Multiple Model Fault Diagnosis for PEM Fuel Cell Systems

10.5772/13994 ◽  
2011 ◽  
Author(s):  
Yan Zhou ◽  
Dongli Wang ◽  
Jianxun Li ◽  
Lingzhi Yi ◽  
Huixian Huang
Keyword(s):  
Fuzzy Logic ◽  
Fuel Cell ◽  
Fault Diagnosis ◽  
Pem Fuel Cell ◽  
Multiple Model ◽  
Cell Systems ◽  
Interactive Multiple Model
Sign in / Sign up

Export Citation Format

Share Document