{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T23:57:25Z","timestamp":1773964645821,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China \u201cIntergovernmental International Science and Technology Innovation Cooperation\u201d Key Special Project","award":["2022YFE0196300"],"award-info":[{"award-number":["2022YFE0196300"]}]},{"name":"Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province","award":["2022YFE0196300"],"award-info":[{"award-number":["2022YFE0196300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In modern power systems or new energy power stations, the medium voltage circuit breakers (MVCBs) are becoming more crucial and the operation reliability of the MVCBs could be greatly improved by online monitoring technology. The purpose of this research is to put forward a fault diagnosis approach based on vibration signal envelope analysis, including offline fault feature training and online fault diagnosis. During offline fault feature training, the envelope of the vibration signal is extracted from the historic operation data of the MVCB, and then the typical fault feature vector M is built by using the wavelet packet-energy spectrum. In the online fault diagnosis process, the fault feature vector T is built based on the extracted envelope of the real-time vibration signal, and the MVCB states are assessed by using the distance between the feature vectors T and M. The proposed method only needs to handle the envelope of the vibration signal, which dramatically reduces the signal bandwidth, and then the cost of the processing hardware and software could be cut down.<\/jats:p>","DOI":"10.3390\/s23198331","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T07:37:50Z","timestamp":1696837070000},"page":"8331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Fault Diagnosis of Medium Voltage Circuit Breakers Based on Vibration Signal Envelope Analysis"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6396-6740","authenticated-orcid":false,"given":"Yongbin","family":"Wu","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Southeast University, Nanjing 210096, China"}]},{"given":"Jianzhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southeast University, Nanjing 210096, China"}]},{"given":"Zhengxi","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southeast University, Nanjing 210096, China"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanjing Power Supply Branch Company, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211102, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TPWRD.2020.2991234","article-title":"Condition Monitoring of High Voltage Circuit Breakers: Past to Future","volume":"36","author":"Niayesh","year":"2021","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3353","DOI":"10.1109\/TIA.2022.3159617","article-title":"Few-Shot Transfer Learning with Attention Mechanism for High-Voltage Circuit Breaker Fault Diagnosis","volume":"58","author":"Wang","year":"2022","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1109\/TPWRD.2013.2274750","article-title":"International Surveys on Circuit-Breaker Reliability Data for Substation and System Studies","volume":"29","author":"Janssen","year":"2014","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.epsr.2013.04.014","article-title":"Reliability Estimation of High Voltage SF6 Circuit Breakers by Statistical Analysis on the Basis of the Field Data","volume":"103","author":"Zhang","year":"2013","journal-title":"Electr. Power Syst. Res."},{"key":"ref_5","unstructured":"Zhang, J., Wu, C., Cheng, M., and Jiang, X. (2013, January 26\u201329). Optimal Design of a Tubular Linear Surface-Mounted Permanent Magnet Actuator for High Voltage Breaker. Proceedings of the 2013 International Conference on Electrical Machines and Systems (ICEMS), Busan, Republic of Korea."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1109\/TIM.2019.2913061","article-title":"Fault Detection for High-Voltage Circuit Breakers Based on Time\u2013Frequency Analysis of Switching Transient E-Fields","volume":"69","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.isatra.2020.05.011","article-title":"Multi-Vibration Information Fusion for Detection of HVCB Faults using CART and D\u2013S Evidence Theory","volume":"113","author":"Ma","year":"2021","journal-title":"ISA Trans."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/JSEN.2020.2980081","article-title":"Multi-Sensor Decision Approach for HVCB Fault Detection Based on the Vibration Information","volume":"21","author":"Ma","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.neucom.2017.02.042","article-title":"Adaptive Fault Diagnosis of HVCBs Based on P-SVDD and P-KFCM","volume":"240","author":"Zhu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"110894","DOI":"10.1016\/j.measurement.2022.110894","article-title":"Fault Diagnosis of High Voltage Circuit Breaker Based on Multi-Sensor Information Fusion with Training Weights","volume":"192","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/TPWRD.2013.2276630","article-title":"Circuit-Breaker Automated Failure Tracking Based on Coil Current Signature","volume":"29","author":"Vakilian","year":"2014","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.21595\/jve.2019.20781","article-title":"Fault Diagnosis Method for Energy Storage Mechanism of High Voltage Circuit Breaker Based on CNN Characteristic Matrix Constructed by Sound-Vibration Signal","volume":"6","author":"Zhao","year":"2019","journal-title":"J. Vibroengineering"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3271042","DOI":"10.1155\/2016\/3271042","article-title":"A Fault Diagnosis Method of High Voltage Circuit Breaker Based on Moving Contact Motion Trajectory and ELM","volume":"2016","author":"Niu","year":"2016","journal-title":"Math. Probl. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.3906\/elk-1508-73","article-title":"Model-Based Fault Analysis of a High-Voltage Circuit Breaker Operating Mechanism","volume":"25","author":"Forootani","year":"2017","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"4154","DOI":"10.1109\/TIM.2019.2946470","article-title":"Fault Identification for Circuit Breakers Based on Vibration Measurements","volume":"69","author":"Yang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_17","first-page":"101757","article-title":"Mechanical Fault Diagnosis for High-Voltage Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine","volume":"2015","author":"Zhang","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"60091","DOI":"10.1109\/ACCESS.2019.2915252","article-title":"Mechanical Faults Diagnosis of High-Voltage Circuit Breaker Via Hybrid Features and Integrated Extreme Learning Machine","volume":"7","author":"Gao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_19","first-page":"3500613","article-title":"A Newly Designed Diagnostic Method for Mechanical Faults of High-Voltage Circuit Breakers Via SSAE and IELM","volume":"70","author":"Gao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, N., Chen, H., Cai, G., Fang, L., and Wang, Y. (2016). Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-layer Classifier. Sensors, 16.","DOI":"10.3390\/s16111887"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dou, L., Wan, S., and Zhan, C. (2018). Application of Multiscale Entropy in Mechanical Fault Diagnosis of High Voltage Circuit Breaker. Entropy, 20.","DOI":"10.3390\/e20050325"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"14070","DOI":"10.1109\/ACCESS.2019.2893922","article-title":"A New Vibration Analysis Approach for Detecting Mechanical Anomalies on Power Circuit Breakers","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"177767","DOI":"10.1109\/ACCESS.2020.3027478","article-title":"Mechanical Fault Diagnosis of High Voltage Circuit Breakers Utilizing VMD Based on Improved Time Segment Energy Entropy and a New Hybrid Classifier","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","first-page":"130","article-title":"Multi-Mapping Fault Diagnosis of High Voltage Circuit Breaker Based on Mathematical Morphology and Wavelet Entropy","volume":"5","author":"Ji","year":"2019","journal-title":"CSEE J. Power Energy Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9777","DOI":"10.1109\/TIE.2018.2879308","article-title":"High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder","volume":"66","author":"Ma","year":"2019","journal-title":"IEEE Trans. Ind. Electron."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8331\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:03:25Z","timestamp":1760130205000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8331"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,9]]},"references-count":25,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23198331"],"URL":"https:\/\/doi.org\/10.3390\/s23198331","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,9]]}}}