Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy
In order to improve the identification accuracy of the high voltage circuit breakers’ (HVCBs) mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD) and fuzzy c-means (FCM)...
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MDPI AG
2016-09-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/18/9/322 |
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| author | Nantian Huang Lihua Fang Guowei Cai Dianguo Xu Huaijin Chen Yonghui Nie |
| author_facet | Nantian Huang Lihua Fang Guowei Cai Dianguo Xu Huaijin Chen Yonghui Nie |
| author_sort | Nantian Huang |
| collection | DOAJ |
| description | In order to improve the identification accuracy of the high voltage circuit breakers’ (HVCBs) mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD) and fuzzy c-means (FCM) clustering method based on Local Mean Decomposition (LMD) and time segmentation energy entropy (TSEE) is proposed. Firstly, LMD is used to decompose nonlinear and non-stationary vibration signals of HVCBs into a series of product functions (PFs). Secondly, TSEE is chosen as feature vectors with the superiority of energy entropy and characteristics of time-delay faults of HVCBs. Then, SVDD trained with normal samples is applied to judge mechanical faults of HVCBs. If the mechanical fault is confirmed, the new fault sample and all known fault samples are clustered by FCM with the cluster number of known fault types. Finally, another SVDD trained by the specific fault samples is used to judge whether the fault sample belongs to an unknown type or not. The results of experiments carried on a real SF6 HVCB validate that the proposed fault-detection method is effective for the known faults with training samples and unknown faults without training samples. |
| first_indexed | 2024-04-11T21:52:49Z |
| format | Article |
| id | doaj.art-0c15647befd34236ade3e72d359fab65 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T21:52:49Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-0c15647befd34236ade3e72d359fab652022-12-22T04:01:11ZengMDPI AGEntropy1099-43002016-09-0118932210.3390/e18090322e18090322Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy EntropyNantian Huang0Lihua Fang1Guowei Cai2Dianguo Xu3Huaijin Chen4Yonghui Nie5School of Electrical Engineering, Northeast Dianli University, Jilin 132012, ChinaSchool of Electrical Engineering, Northeast Dianli University, Jilin 132012, ChinaSchool of Electrical Engineering, Northeast Dianli University, Jilin 132012, ChinaDepartment of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering, Northeast Dianli University, Jilin 132012, ChinaSchool of Electrical Engineering, Northeast Dianli University, Jilin 132012, ChinaIn order to improve the identification accuracy of the high voltage circuit breakers’ (HVCBs) mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD) and fuzzy c-means (FCM) clustering method based on Local Mean Decomposition (LMD) and time segmentation energy entropy (TSEE) is proposed. Firstly, LMD is used to decompose nonlinear and non-stationary vibration signals of HVCBs into a series of product functions (PFs). Secondly, TSEE is chosen as feature vectors with the superiority of energy entropy and characteristics of time-delay faults of HVCBs. Then, SVDD trained with normal samples is applied to judge mechanical faults of HVCBs. If the mechanical fault is confirmed, the new fault sample and all known fault samples are clustered by FCM with the cluster number of known fault types. Finally, another SVDD trained by the specific fault samples is used to judge whether the fault sample belongs to an unknown type or not. The results of experiments carried on a real SF6 HVCB validate that the proposed fault-detection method is effective for the known faults with training samples and unknown faults without training samples.http://www.mdpi.com/1099-4300/18/9/322high voltage circuit breakersmechanical fault diagnosislocal mean decompositiontime segmentation energy entropysupport vector data descriptionfuzzy c-means |
| spellingShingle | Nantian Huang Lihua Fang Guowei Cai Dianguo Xu Huaijin Chen Yonghui Nie Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy Entropy high voltage circuit breakers mechanical fault diagnosis local mean decomposition time segmentation energy entropy support vector data description fuzzy c-means |
| title | Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy |
| title_full | Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy |
| title_fullStr | Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy |
| title_full_unstemmed | Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy |
| title_short | Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy |
| title_sort | mechanical fault diagnosis of high voltage circuit breakers with unknown fault type using hybrid classifier based on lmd and time segmentation energy entropy |
| topic | high voltage circuit breakers mechanical fault diagnosis local mean decomposition time segmentation energy entropy support vector data description fuzzy c-means |
| url | http://www.mdpi.com/1099-4300/18/9/322 |
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