A review on non-model based diagnosis methodologies for PEM fuel cell stacks and systems
A review of non-model based methodologies applied to diagnosis of Proton Exchange Membrane Fuel Cell (PEMFC) system is presented. Three types of non-model based methods including artificial intelligence, statistical method and signal processing method are discussed and compared. The artificial intel...
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Published in | International journal of hydrogen energy Vol. 38; no. 21; pp. 8914 - 8926 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
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Kidlington
Elsevier Ltd
17.07.2013
Elsevier |
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Abstract | A review of non-model based methodologies applied to diagnosis of Proton Exchange Membrane Fuel Cell (PEMFC) system is presented. Three types of non-model based methods including artificial intelligence, statistical method and signal processing method are discussed and compared. The artificial intelligence one, divided into Neural Network (NN), Fuzzy Logic (FL) and neural-fuzzy method, is applied as a fault classifier which is quite different from its role in model-based method. Linear feature reduction methods including Principle Component Analysis (PCA) and Fisher Discriminant Analysis (FDA), and nonlinear ones such as Kernel PCA (KPCA) and Kernel FDA (KFDA) are demonstrated as part of statistical methods. Additionally, a statistical theory based classifier- Bayesian Network (BN) is also introduced in this part. As for signal processing method, both Fast Fourier Transform (FFT) for stationary signals and short-time Fourier Transform (STFT), as well as Wavelet Transform (WT) for non-stationary signals are introduced. Since each method has its advantages and limitations, a comparison is made finally and hybrid approaches resulting from integration of different methods are believed to be promising.
•Non-model based methodologies applied to diagnosis of PEMFC system are summarized.•AI method, statistical method and signal processing one are discussed.•Hybrid approaches resulting from the above methods are believed to be promising.•A general structure of the hybrid approach composed of four steps is given. |
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AbstractList | A review of non-model based methodologies applied to diagnosis of Proton Exchange Membrane Fuel Cell (PEMFC) system is presented. Three types of non-model based methods including artificial intelligence, statistical method and signal processing method are discussed and compared. The artificial intelligence one, divided into Neural Network (NN), Fuzzy Logic (FL) and neural-fuzzy method, is applied as a fault classifier which is quite different from its role in model-based method. Linear feature reduction methods including Principle Component Analysis (PCA) and Fisher Discriminant Analysis (FDA), and nonlinear ones such as Kernel PCA (KPCA) and Kernel FDA (KFDA) are demonstrated as part of statistical methods. Additionally, a statistical theory based classifier- Bayesian Network (BN) is also introduced in this part. As for signal processing method, both Fast Fourier Transform (FFT) for stationary signals and short-time Fourier Transform (STFT), as well as Wavelet Transform (WT) for non-stationary signals are introduced. Since each method has its advantages and limitations, a comparison is made finally and hybrid approaches resulting from integration of different methods are believed to be promising. A review of non-model based methodologies applied to diagnosis of Proton Exchange Membrane Fuel Cell (PEMFC) system is presented. Three types of non-model based methods including artificial intelligence, statistical method and signal processing method are discussed and compared. The artificial intelligence one, divided into Neural Network (NN), Fuzzy Logic (FL) and neural-fuzzy method, is applied as a fault classifier which is quite different from its role in model-based method. Linear feature reduction methods including Principle Component Analysis (PCA) and Fisher Discriminant Analysis (FDA), and nonlinear ones such as Kernel PCA (KPCA) and Kernel FDA (KFDA) are demonstrated as part of statistical methods. Additionally, a statistical theory based classifier- Bayesian Network (BN) is also introduced in this part. As for signal processing method, both Fast Fourier Transform (FFT) for stationary signals and short-time Fourier Transform (STFT), as well as Wavelet Transform (WT) for non-stationary signals are introduced. Since each method has its advantages and limitations, a comparison is made finally and hybrid approaches resulting from integration of different methods are believed to be promising. •Non-model based methodologies applied to diagnosis of PEMFC system are summarized.•AI method, statistical method and signal processing one are discussed.•Hybrid approaches resulting from the above methods are believed to be promising.•A general structure of the hybrid approach composed of four steps is given. |
Author | Becherif, M. Péra, M.C. Petrone, R. Yousfi Steiner, N. Sorrentino, M. Zheng, Z. Hissel, D. Pianese, C. |
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Keywords | Statistical method Signal processing PEMFC Artificial intelligence Non-model based diagnosis Hydrogen Review Fuel cell |
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Snippet | A review of non-model based methodologies applied to diagnosis of Proton Exchange Membrane Fuel Cell (PEMFC) system is presented. Three types of non-model... |
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SubjectTerms | Alternative fuels. Production and utilization Applied sciences Artificial Intelligence Computer Science Diagnosis Electric power Energy Engineering Sciences Exact sciences and technology Expert systems Fourier transforms Fuel cells Fuels Hydrogen Neural networks Non-model based diagnosis PEMFC Signal and Image Processing Signal processing Statistical method Statistical methods |
Title | A review on non-model based diagnosis methodologies for PEM fuel cell stacks and systems |
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