Research on Fuel Cell Fault Diagnosis Based on Genetic Algorithm Optimization of Support Vector Machine
The fuel cell engine mechanism model is used to research fault diagnosis based on a data-driven method to identify the failure of proton exchange membrane fuel cells in the process of operation, which leads to the degradation of system performance and other problems. In this paper, an extreme learni...
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| Published in | Energies (Basel) Vol. 15; no. 6; p. 2294 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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01.03.2022
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| Abstract | The fuel cell engine mechanism model is used to research fault diagnosis based on a data-driven method to identify the failure of proton exchange membrane fuel cells in the process of operation, which leads to the degradation of system performance and other problems. In this paper, an extreme learning machine and a support vector machine are applied to classify the usual faults of fuel cells, including air compressor faults, air supply pipe and return pipe leaks, stack flooding faults and temperature controller faults. The accuracy of fault classification was 78.67% and 83.33% respectively. In order to improve the efficiency of fault classification, a genetic algorithm is used to optimize the parameters of the support vector machine. The simulation results show that the accuracy of fault classification was improved to 94% after optimization. |
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| AbstractList | The fuel cell engine mechanism model is used to research fault diagnosis based on a data-driven method to identify the failure of proton exchange membrane fuel cells in the process of operation, which leads to the degradation of system performance and other problems. In this paper, an extreme learning machine and a support vector machine are applied to classify the usual faults of fuel cells, including air compressor faults, air supply pipe and return pipe leaks, stack flooding faults and temperature controller faults. The accuracy of fault classification was 78.67% and 83.33% respectively. In order to improve the efficiency of fault classification, a genetic algorithm is used to optimize the parameters of the support vector machine. The simulation results show that the accuracy of fault classification was improved to 94% after optimization. |
| Author | Ren, Qiang Li, Weier Sun, Chao Huo, Weiwei Gong, Guoqing |
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| SubjectTerms | Classification Efficiency extreme learning machine Fault diagnosis fuel cell Fuel cell vehicles genetic algorithm Genetic algorithms Hydrogen Neural networks Neurons Sensors support vector machine Support vector machines |
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| Title | Research on Fuel Cell Fault Diagnosis Based on Genetic Algorithm Optimization of Support Vector Machine |
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