{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:27:51Z","timestamp":1773779271296,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2015,12,26]],"date-time":"2015-12-26T00:00:00Z","timestamp":1451088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Mechanical faults of high voltage circuit breakers (HVCBs) are one of the most important factors that affect the reliability of power system operation. Because of the limitation of a lack of samples of each fault type; some fault conditions can be recognized as a normal condition. The fault diagnosis results of HVCBs seriously affect the operation reliability of the entire power system. In order to improve the fault diagnosis accuracy of HVCBs; a method for mechanical fault diagnosis of HVCBs based on wavelet time-frequency entropy (WTFE) and one-class support vector machine (OCSVM) is proposed. In this method; the S-transform (ST) is proposed to analyze the energy time-frequency distribution of HVCBs\u2019 vibration signals. Then; WTFE is selected as the feature vector that reflects the information characteristics of vibration signals in the time and frequency domains. OCSVM is used for judging whether a mechanical fault of HVCBs has occurred or not. In order to improve the fault detection accuracy; a particle swarm optimization (PSO) algorithm is employed to optimize the parameters of OCSVM; including the window width of the kernel function and error limit. If the mechanical fault is confirmed; a support vector machine (SVM)-based classifier will be used to recognize the fault type. The experiments carried on a real SF6 HVCB demonstrated the improved effectiveness of the new approach.<\/jats:p>","DOI":"10.3390\/e18010007","type":"journal-article","created":{"date-parts":[[2015,12,28]],"date-time":"2015-12-28T04:23:49Z","timestamp":1451276629000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine"],"prefix":"10.3390","volume":"18","author":[{"given":"Nantian","family":"Huang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3913-9029","authenticated-orcid":false,"given":"Huaijin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China"}]},{"given":"Shuxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China"}]},{"given":"Guowei","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China"}]},{"given":"Weiguo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China"}]},{"given":"Dianguo","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Lihua","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China"}]}],"member":"1968","published-online":{"date-parts":[[2015,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1109\/61.141847","article-title":"Acoustic diagnosis of high voltage circuit-breakers","volume":"7","author":"Runde","year":"1992","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1109\/61.127063","article-title":"A noninvasive diagnostic instrument for power circuit breakers","volume":"7","author":"Demjanenko","year":"1992","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/61.489343","article-title":"Event timing and shape analysis of vibration bursts from power circuit breakers","volume":"11","author":"Polycarpou","year":"1995","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1109\/61.544262","article-title":"Vibration analysis for diagnostic testing of circuit-breakers","volume":"11","author":"Runde","year":"1996","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_5","unstructured":"CIGRE Working Group (1994). Final Report of the Second International Enquiry on High Voltage Circuit Breaker Failures and Defects in Service, CIGRE. CIGRE Report No. 83."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TIE.2015.2417501","article-title":"A survey of fault diagnosis and fault-tolerant techniques-Part I: Fault diagnosis with model-based and signal-based approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPWRD.2005.855475","article-title":"The detection of the closing moments of a vacuum circuit breaker by vibration analysis","volume":"21","author":"Meng","year":"2006","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1109\/TPWRD.2008.2002846","article-title":"An improved vibration analysis algorithm as a diagnostic tool for detecting mechanical anomalies on power circuit breakers","volume":"23","author":"Landry","year":"2008","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1109\/TPWRD.2003.809615","article-title":"New fault diagnosis of circuit breakers","volume":"18","author":"Lee","year":"2003","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1016\/j.measurement.2011.02.017","article-title":"Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker","volume":"44","author":"Huang","year":"2011","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.epsr.2010.10.029","article-title":"An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine","volume":"81","author":"Huang","year":"2011","journal-title":"Electr. Power Syst. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.1109\/TPWRD.2005.855486","article-title":"Continuous monitoring of circuit-breakers using vibration analysis","volume":"20","author":"Runde","year":"2005","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/78.492555","article-title":"Localization of the complex spectrum: The S transform","volume":"44","author":"Stockwell","year":"1996","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_15","unstructured":"He, Z.Y., Chen, X.Q., and Luo, G.M. (2006, January 22\u201326). Wavelet entropy measure definition and its application for transmission line fault detection and identification. Proceedings of the International Conference on Power System Technology, Chongqing, China."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.3390\/e16063009","article-title":"Tsallis wavelet entropy and its application in power signal analysis","volume":"16","author":"Chen","year":"2014","journal-title":"Entropy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.ijepes.2010.10.001","article-title":"Study of a new method for power system transients classification based on wavelet entropy and neural network","volume":"33","author":"He","year":"2011","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.neucom.2013.11.009","article-title":"Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine","volume":"133","author":"Kumar","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5218","DOI":"10.3390\/e17085218","article-title":"An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures","volume":"17","author":"Sharma","year":"2015","journal-title":"Entropy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.ymssp.2012.04.016","article-title":"Fault detection of mechanical drives under variable operating conditions based on wavelet packet R\u00e9nyi entropy signatures","volume":"31","year":"2012","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1049\/iet-gtd.2012.0528","article-title":"Wavelet singular entropy-based symmetrical fault-detection and out-of-step protection during power swing","volume":"7","author":"Dubey","year":"2013","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neucom.2013.03.059","article-title":"Real-time fault diagnosis for gas turbine generator systems using extreme learning machine","volume":"128","author":"Wong","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, Z.X., Wong, P.K., Vong, C.M., Zhong, J.H., and Liang, J.J. (2013). Simultaneous-fault diagnosis of gas turbine generator systems using a pairwise-coupled probabilistic classifier. Math. Probl. Eng., 2013.","DOI":"10.1155\/2013\/827128"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Platt","year":"2001","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1016\/j.jprocont.2009.07.011","article-title":"Fault detection and diagnosis in process data using one-class support vector machines","volume":"19","author":"Mahadevan","year":"2009","journal-title":"J. Process Control"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.cie.2005.01.009","article-title":"One-class support vector machines\u2014An application in machine fault detection and classification","volume":"48","author":"Shin","year":"2005","journal-title":"Comput. Ind. Eng."},{"key":"ref_27","first-page":"927","article-title":"Parameter selection of Gaussian kernel for one-class SVM","volume":"45","author":"Xiao","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_28","unstructured":"Kennedy, J., and Eberhart, R.C. (December, January 27). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Piscataway, NJ, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1109\/TPWRD.2007.911125","article-title":"Detection and classification of power quality disturbances using S-transform and probabilistic neural network","volume":"23","author":"Mishra","year":"2008","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_30","unstructured":"Olsson, A.E. (2011). Particle Swarm Optimization: Theory, Techniques and Applications, Nova Science Publishers."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/18\/1\/7\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:54:53Z","timestamp":1760216093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/18\/1\/7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,12,26]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,1]]}},"alternative-id":["e18010007"],"URL":"https:\/\/doi.org\/10.3390\/e18010007","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,12,26]]}}}