scholarly journals Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy

Entropy ◽  
2016 ◽  
Vol 18 (9) ◽  
pp. 322 ◽  
Author(s):  
Nantian Huang ◽  
Lihua Fang ◽  
Guowei Cai ◽  
Dianguo Xu ◽  
Huaijin Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Voltage ◽  
Circuit Breakers ◽  
Hybrid Classifier ◽  
Fault Type ◽  
Energy Entropy ◽  
Time Segmentation

scholarly journals A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 288 ◽  
Author(s):  
Lin Lin ◽  
Bin Wang ◽  
Jiajin Qi ◽  
Lingling Chen ◽  
Nantian Huang
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
High Voltage ◽  
Vibration Signal ◽  
Circuit Breakers ◽  
Optimal Subset ◽  
Fault Type ◽  
Diagnosis Approach

The reliability and performance of high-voltage circuit breakers (HVCBs) will directly affect the safety and stability of the power system itself, and mechanical failures of HVCBs are one of the important factors affecting the reliability of circuit breakers. Moreover, the existing fault diagnosis methods for circuit breakers are complex and inefficient in feature extraction. To improve the efficiency of feature extraction, a novel mechanical fault feature selection and diagnosis approach for high-voltage circuit breakers, using features extracted without signal processing is proposed. Firstly, the vibration signal of the HVCBs’ operating system, which collects the amplitudes of signals from normal vibration signals, is segmented by a time scale, and obviously changed. Adopting the ensemble learning method, features were extracted from each part of the divided signal, and used for constructing a vector. The Gini importance of features is obtained by random forest (RF), and the feature is ranked by the features’ importance index. After that, sequential forward selection (SFS) is applied to determine the optimal subset, while the regularized Fisher’s criterion (RFC) is used to analyze the classification ability. Then, the optimal subset is input to the hierarchical hybrid classifier, and based on a one-class support vector machine (OCSVM) and RF for fault diagnosis, the state is accurately recognized by OCSVM. The known fault types are identified using RF, and the identification results are calibrated with OCSVM of a particular fault type. The experimental proves that the new method has high feature extraction efficiency and recognition accuracy by the measured HVCBs vibration signal, while the unknown fault type data of the untrained samples is effectively identified.

scholarly journals Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine

Entropy ◽  
2015 ◽  
Vol 18 (1) ◽  
pp. 7 ◽  
Author(s):  
Nantian Huang ◽  
Huaijin Chen ◽  
Shuxin Zhang ◽  
Guowei Cai ◽  
Weiguo Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
High Voltage ◽  
Support Vector ◽  
Circuit Breakers ◽  
Time Frequency
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