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.

Research on Acoustic Mechanical Fault Diagnosis Method of High Voltage Circuit Breaker Based on Improved EEMD

2014 ◽  
Vol 687-691 ◽  
pp. 1054-1057 ◽  
Author(s):  
Xian Ping Zhao ◽  
Zhi Wan Cheng ◽  
Xiang Yu Tan ◽  
Wei Hua Niu
Keyword(s):  
Fault Diagnosis ◽  
High Voltage ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Fault Classification ◽  
Support Vector ◽  
Circuit Breakers ◽  
High Voltage Circuit Breaker ◽  
Fast Independent Component Analysis ◽  
Diagnosis Method

High voltage circuit breaker is one of the most significant devices and its health status will impact security of the power system. In this paper, the method of high voltage circuit breakers mechanical fault diagnosis is discussed, fault diagnosis method based on vibration signal is proposed. Firstly, the collected acoustic signals are proceed by blind source separation processing through fast independent component analysis. Then, the acoustic signal feature vector is extracted by improved ensemble empirical mode decomposition (EEMD) and the residual signal is filtered by fractional differential. Finally, the feature vectors are input into support vector machine (SVM) for fault diagnosis. Experiment shows that the proposed method can get more precise fault classification to high voltage circuit breakers.

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 Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 23903-23926 ◽  
Author(s):  
Mariela Cerrada ◽  
René Sánchez ◽  
Diego Cabrera ◽  
Grover Zurita ◽  
Chuan Li
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Multi Stage

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

scholarly journals Engine Fault Detection Approach Based on Angle Domain Signal Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012034
Author(s):  
Hai Zeng ◽  
Ning Zeng ◽  
Jin Han ◽  
Yan Ding
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Failure Detection ◽  
Vibration Signal ◽  
Bench Test ◽  
Signal Model ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Detection Approach ◽  
Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

scholarly journals Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zong Yuan ◽  
Taotao Zhou ◽  
Jie Liu ◽  
Changhe Zhang ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Ranking And Selection ◽  
Fault Features ◽  
Feature Importance ◽  
Importance Ranking ◽  
Common Signal ◽  
Diagnosis Approach

The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.

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