An application of ensemble empirical mode decomposition and correlation dimension for the HV circuit breaker diagnosis
During the operation process of the high-voltage circuit breaker, the changes of vibration signals reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an ex...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-01-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/00051144.2019.1578037 |
| _version_ | 1818897195384963072 |
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| author | Mingliang Liu Bing Li Jianfeng Zhang Keqi Wang |
| author_facet | Mingliang Liu Bing Li Jianfeng Zhang Keqi Wang |
| author_sort | Mingliang Liu |
| collection | DOAJ |
| description | During the operation process of the high-voltage circuit breaker, the changes of vibration signals reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition) and correlation dimension and a classification method with BP (back propagation) neural network. Firstly, original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, correlation dimension of the top four IMFs by the G–P algorithm is calculated and the characteristic vector of the vibration signal of the circuit breaker is formed. At last, the classification of characteristic parameter is realized with a simple BP neural network for fault diagnosis. The experimentation without loads indicates that the method can easily and accurately diagnose breaker faults and exploit a new road for diagnosis of high-voltage circuit breakers. |
| first_indexed | 2024-12-19T19:12:19Z |
| format | Article |
| id | doaj.art-ce78ce1d4c6e42859a454bd3e3b7e713 |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-12-19T19:12:19Z |
| publishDate | 2019-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-ce78ce1d4c6e42859a454bd3e3b7e7132022-12-21T20:09:14ZengTaylor & Francis GroupAutomatika0005-11441848-33802019-01-0160110511210.1080/00051144.2019.15780371578037An application of ensemble empirical mode decomposition and correlation dimension for the HV circuit breaker diagnosisMingliang Liu0Bing Li1Jianfeng Zhang2Keqi Wang3Heilongjiang UniversityHeilongjiang UniversityHeilongjiang UniversityNortheast Forestry UniversityDuring the operation process of the high-voltage circuit breaker, the changes of vibration signals reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition) and correlation dimension and a classification method with BP (back propagation) neural network. Firstly, original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, correlation dimension of the top four IMFs by the G–P algorithm is calculated and the characteristic vector of the vibration signal of the circuit breaker is formed. At last, the classification of characteristic parameter is realized with a simple BP neural network for fault diagnosis. The experimentation without loads indicates that the method can easily and accurately diagnose breaker faults and exploit a new road for diagnosis of high-voltage circuit breakers.http://dx.doi.org/10.1080/00051144.2019.1578037High-voltage circuit breakervibration signalensemble empirical mode decompositioncorrelation dimensionBP neural networkfault diagnosis |
| spellingShingle | Mingliang Liu Bing Li Jianfeng Zhang Keqi Wang An application of ensemble empirical mode decomposition and correlation dimension for the HV circuit breaker diagnosis Automatika High-voltage circuit breaker vibration signal ensemble empirical mode decomposition correlation dimension BP neural network fault diagnosis |
| title | An application of ensemble empirical mode decomposition and correlation dimension for the HV circuit breaker diagnosis |
| title_full | An application of ensemble empirical mode decomposition and correlation dimension for the HV circuit breaker diagnosis |
| title_fullStr | An application of ensemble empirical mode decomposition and correlation dimension for the HV circuit breaker diagnosis |
| title_full_unstemmed | An application of ensemble empirical mode decomposition and correlation dimension for the HV circuit breaker diagnosis |
| title_short | An application of ensemble empirical mode decomposition and correlation dimension for the HV circuit breaker diagnosis |
| title_sort | application of ensemble empirical mode decomposition and correlation dimension for the hv circuit breaker diagnosis |
| topic | High-voltage circuit breaker vibration signal ensemble empirical mode decomposition correlation dimension BP neural network fault diagnosis |
| url | http://dx.doi.org/10.1080/00051144.2019.1578037 |
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