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Sensors 2019, 19(2), 288; https://doi.org/10.3390/s19020288

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

1
College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
2
Taian Power Supply Company, State Grid Shandong Electric Power Company, Taian 271000, China
3
Hangzhou Municipal Electric Power Supply Company of State Grid, Hangzhou 310009, China
4
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
*
Author to whom correspondence should be addressed.
Received: 12 December 2018 / Revised: 7 January 2019 / Accepted: 7 January 2019 / Published: 12 January 2019
(This article belongs to the Special Issue Piezoelectric Transducers: Advances in Structural Health Monitoring)
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Abstract

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. View Full-Text
Keywords: high voltage circuit breaker; one-class support vector machine; random forest high voltage circuit breaker; one-class support vector machine; random forest
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Lin, L.; Wang, B.; Qi, J.; Chen, L.; Huang, N. A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing. Sensors 2019, 19, 288.

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