Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification
Data-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose faults through the state variable data collected during the operation of the PEMFC system. However, the state variable data collected from the PEMFC system during the stack switching between d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/7/1918 |
| _version_ | 1827696327987822592 |
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| author | Ying Tian Qiang Zou Jin Han |
| author_facet | Ying Tian Qiang Zou Jin Han |
| author_sort | Ying Tian |
| collection | DOAJ |
| description | Data-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose faults through the state variable data collected during the operation of the PEMFC system. However, the state variable data collected from the PEMFC system during the stack switching between different operating points can easily cause false alarms, such that the practical value of the diagnosis system is reduced. To overcome this problem, a fault diagnosis method for PEMFC systems based on steady-state identification is proposed in this paper. The support vector data description (SVDD) and relevance vector machine (RVM) optimized by the artificial bee colony (ABC) are used for the steady-state identification and fault diagnosis. The density-based spatial clustering of applications with noise (DBSCAN) and linear least squares fitting (LLSF) are used to identify the abnormal data in datasets and estimate change rates of the system state variables respectively. The proposed method can automatically identify the state variable data collected from the PEMFC system during the stack switching between different operating points, so that the diagnosis accuracy can be improved and false alarms can be reduced. The proposed method has a certain practical value and can provide a reference for further study. |
| first_indexed | 2024-03-10T12:45:08Z |
| format | Article |
| id | doaj.art-0d2199677a6e45a28eb90bd896a1eb64 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T12:45:08Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0d2199677a6e45a28eb90bd896a1eb642023-11-21T13:30:05ZengMDPI AGEnergies1996-10732021-03-01147191810.3390/en14071918Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State IdentificationYing Tian0Qiang Zou1Jin Han2Beijing 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, ChinaData-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose faults through the state variable data collected during the operation of the PEMFC system. However, the state variable data collected from the PEMFC system during the stack switching between different operating points can easily cause false alarms, such that the practical value of the diagnosis system is reduced. To overcome this problem, a fault diagnosis method for PEMFC systems based on steady-state identification is proposed in this paper. The support vector data description (SVDD) and relevance vector machine (RVM) optimized by the artificial bee colony (ABC) are used for the steady-state identification and fault diagnosis. The density-based spatial clustering of applications with noise (DBSCAN) and linear least squares fitting (LLSF) are used to identify the abnormal data in datasets and estimate change rates of the system state variables respectively. The proposed method can automatically identify the state variable data collected from the PEMFC system during the stack switching between different operating points, so that the diagnosis accuracy can be improved and false alarms can be reduced. The proposed method has a certain practical value and can provide a reference for further study.https://www.mdpi.com/1996-1073/14/7/1918PEMFC systemfault diagnosissteady-state identificationrelevance vector machine |
| spellingShingle | Ying Tian Qiang Zou Jin Han Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification Energies PEMFC system fault diagnosis steady-state identification relevance vector machine |
| title | Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification |
| title_full | Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification |
| title_fullStr | Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification |
| title_full_unstemmed | Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification |
| title_short | Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification |
| title_sort | data driven fault diagnosis for automotive pemfc systems based on the steady state identification |
| topic | PEMFC system fault diagnosis steady-state identification relevance vector machine |
| url | https://www.mdpi.com/1996-1073/14/7/1918 |
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