دورية أكاديمية
Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy
| العنوان: | Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy |
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| المؤلفون: | Nantian Huang, Lihua Fang, Guowei Cai, Dianguo Xu, Huaijin Chen, Yonghui Nie |
| المصدر: | Entropy, Vol 18, Iss 9, p 322 (2016) |
| بيانات النشر: | MDPI AG, 2016. |
| سنة النشر: | 2016 |
| المجموعة: | LCC:Science LCC:Astrophysics LCC:Physics |
| مصطلحات موضوعية: | high voltage circuit breakers, mechanical fault diagnosis, local mean decomposition, time segmentation energy entropy, support vector data description, fuzzy c-means, Science, Astrophysics, QB460-466, Physics, QC1-999 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1099-4300 |
| Relation: | http://www.mdpi.com/1099-4300/18/9/322; https://doaj.org/toc/1099-4300 |
| DOI: | 10.3390/e18090322 |
| URL الوصول: | https://doaj.org/article/0c15647befd34236ade3e72d359fab65 |
| رقم الأكسشن: | edsdoj.0c15647befd34236ade3e72d359fab65 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 10994300 |
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| DOI: | 10.3390/e18090322 |