A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry
The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/20/3200 |
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| author | Chao Cheng Weijun Wang He Di Xuedong Li Haotong Lv Zhiwei Wan |
| author_facet | Chao Cheng Weijun Wang He Di Xuedong Li Haotong Lv Zhiwei Wan |
| author_sort | Chao Cheng |
| collection | DOAJ |
| description | The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance. |
| format | Article |
| id | doaj-art-0767ba5b4e5f4f199af3b7a159a9cf8d |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-0767ba5b4e5f4f199af3b7a159a9cf8d2025-08-20T02:10:56ZengMDPI AGMathematics2227-73902024-10-011220320010.3390/math12203200A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of IndustryChao Cheng0Weijun Wang1He Di2Xuedong Li3Haotong Lv4Zhiwei Wan5Department of Computer Science and Engineering, Changchun University of Technology, Changchun 130000, ChinaDepartment of Mathematics and Statistics, Changchun University of Technology, Changchun 130000, ChinaDepartment of Communication Engineering, Jilin University, Changchun 130000, ChinaDepartment of Computer Science and Engineering, Changchun University of Technology, Changchun 130000, ChinaDepartment of Computer Science and Engineering, Changchun University of Technology, Changchun 130000, ChinaDepartment of Computer Science and Engineering, Changchun University of Technology, Changchun 130000, ChinaThe improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance.https://www.mdpi.com/2227-7390/12/20/3200fault detectionfault estimationsubspace identificationelectrical systemsKalman filter |
| spellingShingle | Chao Cheng Weijun Wang He Di Xuedong Li Haotong Lv Zhiwei Wan A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry Mathematics fault detection fault estimation subspace identification electrical systems Kalman filter |
| title | A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry |
| title_full | A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry |
| title_fullStr | A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry |
| title_full_unstemmed | A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry |
| title_short | A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry |
| title_sort | martingale posterior based fault detection and estimation method for electrical systems of industry |
| topic | fault detection fault estimation subspace identification electrical systems Kalman filter |
| url | https://www.mdpi.com/2227-7390/12/20/3200 |
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