Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System
In this work, the possibilistic fuzzy C-means clustering artificial bee colony support vector machine (PFCM-ABC-SVM) method is proposed and applied for the fault diagnosis of a polymer electrolyte membrane (PEM) fuel cell system. The innovation of this method is that it can filter data with Gaussian...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/10/2531 |
| _version_ | 1827716816943710208 |
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| author | Feng Han Ying Tian Qiang Zou Xin Zhang |
| author_facet | Feng Han Ying Tian Qiang Zou Xin Zhang |
| author_sort | Feng Han |
| collection | DOAJ |
| description | In this work, the possibilistic fuzzy C-means clustering artificial bee colony support vector machine (PFCM-ABC-SVM) method is proposed and applied for the fault diagnosis of a polymer electrolyte membrane (PEM) fuel cell system. The innovation of this method is that it can filter data with Gaussian noise and diagnose faults under dynamic conditions, and the amplitude of characteristic parameters is reduced to ±10%. Under dynamic conditions with Gaussian noise, the faults of the PEM fuel cell system are simulated and the original dataset is established. The possibilistic fuzzy C-means (PFCM) algorithm is used to filter samples with membership and typicality less than 90% and to optimize the original dataset. The artificial bee colony (ABC) algorithm is used to optimize the penalty factor <i>C</i> and kernel function parameter <i>g</i>. Finally, the optimized support vector machine (SVM) model is used to diagnose the faults of the PEM fuel cell system. To illustrate the results of the fault diagnosis, a nonlinear PEM fuel cell simulator model which has been presented in the literature is used. In addition, the PFCM-ABC-SVM method is compared with other methods. The result shows that the method can diagnose faults in a PEM fuel cell system effectively and the accuracy of the testing set sample is up to 98.51%. When solving small-sized, nonlinear, high-dimensional problems, the PFCM-ABC-SVM method can improve the accuracy of fault diagnosis. |
| first_indexed | 2024-03-10T19:48:08Z |
| format | Article |
| id | doaj.art-ec576124d8ad4280b4db408561e6d980 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T19:48:08Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ec576124d8ad4280b4db408561e6d9802023-11-20T00:41:37ZengMDPI AGEnergies1996-10732020-05-011310253110.3390/en13102531Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell SystemFeng Han0Ying Tian1Qiang Zou2Xin Zhang3Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaIn this work, the possibilistic fuzzy C-means clustering artificial bee colony support vector machine (PFCM-ABC-SVM) method is proposed and applied for the fault diagnosis of a polymer electrolyte membrane (PEM) fuel cell system. The innovation of this method is that it can filter data with Gaussian noise and diagnose faults under dynamic conditions, and the amplitude of characteristic parameters is reduced to ±10%. Under dynamic conditions with Gaussian noise, the faults of the PEM fuel cell system are simulated and the original dataset is established. The possibilistic fuzzy C-means (PFCM) algorithm is used to filter samples with membership and typicality less than 90% and to optimize the original dataset. The artificial bee colony (ABC) algorithm is used to optimize the penalty factor <i>C</i> and kernel function parameter <i>g</i>. Finally, the optimized support vector machine (SVM) model is used to diagnose the faults of the PEM fuel cell system. To illustrate the results of the fault diagnosis, a nonlinear PEM fuel cell simulator model which has been presented in the literature is used. In addition, the PFCM-ABC-SVM method is compared with other methods. The result shows that the method can diagnose faults in a PEM fuel cell system effectively and the accuracy of the testing set sample is up to 98.51%. When solving small-sized, nonlinear, high-dimensional problems, the PFCM-ABC-SVM method can improve the accuracy of fault diagnosis.https://www.mdpi.com/1996-1073/13/10/2531fault diagnosisPEM fuel cell systemPFCM-ABC-SVM |
| spellingShingle | Feng Han Ying Tian Qiang Zou Xin Zhang Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System Energies fault diagnosis PEM fuel cell system PFCM-ABC-SVM |
| title | Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System |
| title_full | Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System |
| title_fullStr | Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System |
| title_full_unstemmed | Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System |
| title_short | Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System |
| title_sort | research on the fault diagnosis of a polymer electrolyte membrane fuel cell system |
| topic | fault diagnosis PEM fuel cell system PFCM-ABC-SVM |
| url | https://www.mdpi.com/1996-1073/13/10/2531 |
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