scholarly journals Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 478 ◽  
Author(s):  
Jiajin Qi ◽  
Xu Gao ◽  
Nantian Huang
Keyword(s):  
Feature Extraction ◽  
High Voltage ◽  
Time Domain ◽  
High Efficiency ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Feature Subset ◽  
High Voltage Circuit Breaker ◽  
Optimal Feature Subset ◽  
Optimal Feature

The fault samples of high voltage circuit breakers are few, the vibration signals are complex, the existing research methods cannot extract the effective information in the features, and it is easy to overfit, slow training, and other problems. To improve the efficiency of feature extraction of a circuit breaker vibration signal and the accuracy of circuit breaker state recognition, a Light Gradient Boosting Machine (LightGBM) method based on time-domain feature extraction with multi-type entropy features for mechanical fault diagnosis of the high voltage circuit breaker is proposed. First, the original vibration signal of the high voltage circuit breaker is segmented in the time domain; then, 16 features including 5 kinds of entropy features are extracted directly from each part of the original signal after time-domain segmentation, and the original feature set is constructed. Second, the Split importance value of each feature is calculated, and the optimal feature subset is determined by the forward feature selection, taking the classification accuracy of LightGBM as the decision variable. After that, the LightGBM classifier is constructed based on the feature vector of the optimal feature subset, which can accurately distinguish the mechanical fault state of the high voltage circuit breaker. The experimental results show that the new method has the advantages of high efficiency of feature extraction and high accuracy of fault identification.

scholarly journals Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals

2019 ◽  
Vol 10 ◽  
pp. 13 ◽  
Author(s):  
Long Li ◽  
Jianfeng Xiao ◽  
Bin Wu ◽  
Mengge Zhou ◽  
Qian Wang
Keyword(s):  
Feature Extraction ◽  
High Voltage ◽  
Extraction Method ◽  
Wavelet Packet ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Normal Operation ◽  
Support Vector ◽  
Feature Extraction Method ◽  
High Voltage Circuit Breaker

The development of power grid system not only increases voltage and capacity, but also increases power risk. This paper briefly introduces the feature extraction method of the vibration signal of high voltage circuit breaker and support vector machine (SVM) algorithm and then analyzed the high voltage circuit breaker in three states: normal operation, fixed screw loosening and falling of opening spring, using the SVM based on the above feature extraction method. The results showed that the accuracy and precision rates of fault identification of circuit breaker were the highest by using the wavelet packet energy entropy extraction features, the false alarm rate was the lowest, and the detection time was the shortest.

Extreme Learning Machine in the Breaker Fault Diagnosis

2014 ◽  
Vol 960-961 ◽  
pp. 896-899
Author(s):  
Dan Jiang ◽  
Shu Tao Zhao ◽  
Jian Feng Ren ◽  
Yu Tao Xu
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
High Voltage ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
High Voltage Circuit Breaker ◽  
Vibration Signal Analysis ◽  
Learning Machine ◽  
Hidden Layer ◽  
New Diagnosis

In order to improve the diagnosis method of the existing high-voltage circuit breaker fault, demonstrated a new diagnosis methord of mechanical failure of high voltage circuit breaker based on vibration signal. According to the factors of high voltage circuit breaker failure and the features of Single-hidden Layer Feedforward Neural Network, SLFN, a method of high voltage circuit breaker fault diagnosis proposed based on Extreme Learning Machine (ELM). Finally, the experiment proves the effectiveness of this method for breaker fault diagnosis based on vibration signal analysis and ELM.

scholarly journals Emotional Classification of Acoustic Information With Optimal Feature Subset Selection Methods

2018 ◽  
Vol 7 (2.32) ◽  
pp. 39
Author(s):  
Dr Swarna Kuchibhotla ◽  
Mr Niranjan M.S.R
Keyword(s):  
High Efficiency ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Computational Time ◽  
Feature Subset ◽  
Selection Methods ◽  
Forward Selection ◽  
Optimal Feature Subset ◽  
Optimal Feature

This paper mainly focuses on classification of various Acoustic emotional corpora with frequency domain features using feature subset selection methods. The emotional speech samples are classified into neutral,  happy, fear , anger,  disgust and sad  states by using properties of statistics  of spectral features estimated from Berlin and Spanish emotional utterances. The Sequential Forward Selection(SFS) and Sequential Floating Forward Selection(SFFS)feature subset selection algorithms are  for extracting more informative features. The number of speech emotional samples available for training is smaller than that of the number of features extracted from the speech sample in both Berlin and Spanish corpora which is called curse of dimensionality. Because of this  feature vector of high dimensionality the efficiency of the classifier decreases and at the same time the computational time also increases. For additional  improvement in the efficiency of the classifier  a subset of  features which are optimal is needed and is obtained by using feature subset selection methods. This will enhances the performance of the system with high efficiency and lower computation time. The classifier used in this work is the standard K Nearest Neighbour (KNN) Classifier. Experimental evaluation   proved  that the performance of the classifier is enhanced with SFFS because it vanishes the nesting effect suffered by SFS. The results also showed that an optimal feature subset is a better choice for classification rather than full feature set.  

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.

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