A Newly Designed Diagnostic Method for Mechanical Faults of High-Voltage Circuit Breakers via SSAE and IELM

In general, vibration signals generated by the switching operation of a high-voltage circuit breaker (HVCB) contains important information to reflect its mechanical status. A method for mechanical fault diagnoses of an HVCB based on a semisupervised stacked autoencoder (SSAE) and an integrated extreme learning machine (IELM) is proposed in this study. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the vibration signal to obtain the time–frequency energy matrix. Then, an SSAE model is applied to automatically extract the characteristic information from the energy matrix. As a result, two-level classifiers can be constructed. The first level is utilized to identify normal or abnormal states, and the second level is selected to identify various types of faults in the abnormal state. The classifiers of these levels are composed of binary IELM. The advantages of the proposed method are that it not only can automatically extract the high-recognition features from the time–frequency energy matrix of high dimension to complete the identification of the existing fault types in the training set but also can accurately identify the samples of unknown types of faults. Experimental results show that the proposed method can effectively diagnose mechanical faults of an HVCB, and the classification accuracy reaches 99.5%.

[1]  Wei Gao,et al.  Mechanical Faults Diagnosis of High-Voltage Circuit Breaker via Hybrid Features and Integrated Extreme Learning Machine , 2019, IEEE Access.

[2]  Jiangjun Ruan,et al.  A New Vibration Analysis Approach for Detecting Mechanical Anomalies on Power Circuit Breakers , 2019, IEEE Access.

[3]  Yuhao Wang,et al.  Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest , 2018, Sensors.

[4]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Bing Li,et al.  Mechanical Fault Diagnosis of High Voltage Circuit Breakers Utilizing EWT-Improved Time Frequency Entropy and Optimal GRNN Classifier , 2018, Entropy.

[6]  Shuting Wan,et al.  Application of Multiscale Entropy in Mechanical Fault Diagnosis of High Voltage Circuit Breaker , 2018, Entropy.

[7]  Guowei Cai,et al.  Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier , 2016, Sensors.

[8]  Ping-Huan Kuo,et al.  Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting , 2019, IEEE Access.

[9]  Liqing Zhang,et al.  Feature learning from incomplete EEG with denoising autoencoder , 2014, Neurocomputing.

[10]  Shaojiang Dong,et al.  Application of fuzzy C-means method and classification model of optimized K-nearest neighbor for fault diagnosis of bearing , 2016 .

[11]  Fenghua Wang,et al.  Fault Diagnosis of On-Load Tap-Changer in Converter Transformer Based on Time–Frequency Vibration Analysis , 2016, IEEE Transactions on Industrial Electronics.

[12]  Hongxun Yao,et al.  Auto-encoder based dimensionality reduction , 2016, Neurocomputing.

[13]  Yuhao Wang,et al.  High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder , 2019, IEEE Transactions on Industrial Electronics.

[14]  Dianguo Xu,et al.  Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine , 2015, Entropy.

[15]  Rui Yang,et al.  An Improved Fault Diagnosis Method of Rotating Machinery Using Sensitive Features and RLS-BP Neural Network , 2020, IEEE Transactions on Instrumentation and Measurement.

[16]  Yi Qin,et al.  A New Family of Model-Based Impulsive Wavelets and Their Sparse Representation for Rolling Bearing Fault Diagnosis , 2018, IEEE Transactions on Industrial Electronics.

[17]  Qian Liu,et al.  Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process , 2019, Reliab. Eng. Syst. Saf..

[18]  Huan Wang,et al.  A Novel Deeper One-Dimensional CNN With Residual Learning for Fault Diagnosis of Wheelset Bearings in High-Speed Trains , 2019, IEEE Access.

[19]  Fan Yang,et al.  Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker , 2011 .

[20]  Anton Janssen,et al.  International Surveys on Circuit-Breaker Reliability Data for Substation and System Studies , 2014, IEEE Transactions on Power Delivery.

[21]  Shuangquan Wang,et al.  A Class Incremental Extreme Learning Machine for Activity Recognition , 2014, Cognitive Computation.

[22]  Chen Lu,et al.  Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification , 2017, Signal Process..

[23]  Vincenzo Conti,et al.  A Novel Technique for Fingerprint Classification Based on Fuzzy C-Means and Naive Bayes Classifier , 2014, 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems.

[24]  Patrick Flandrin,et al.  A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[25]  Gangbing Song,et al.  Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE , 2018, Materials.

[26]  Mingzhe Rong,et al.  The detection of the closing moments of a vacuum circuit breaker by vibration analysis , 2006, IEEE Transactions on Power Delivery.

[27]  Yan Xiao,et al.  Research on optimal identification method of circuit breaker defect type based on phase space reconstruction and SVM , 2019, IEEJ Transactions on Electrical and Electronic Engineering.

[28]  Lin Cheng,et al.  Fault Detection for High-Voltage Circuit Breakers Based on Time–Frequency Analysis of Switching Transient $E$ -Fields , 2020, IEEE Transactions on Instrumentation and Measurement.

[29]  Chee Peng Lim,et al.  A hybrid FAM–CART model and its application to medical data classification , 2015, Neural Computing and Applications.

[30]  Jiangjun Ruan,et al.  Fault Identification for Circuit Breakers Based on Vibration Measurements , 2020, IEEE Transactions on Instrumentation and Measurement.

[31]  Bin Li,et al.  Fault diagnosis method of HV circuit breaker based on wavelet packet time-frequency entropy and BP neural network , 2017, 2017 29th Chinese Control And Decision Conference (CCDC).