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First, the vibration signal of the switch operating mechanism is extracted, the wavelet packet conversion is performed, and the vibration signal of each frequency band is divided into equal times. The energy of the time\u2013frequency subplane of the vibration signal is then calculated, and the time\u2013frequency energy distribution is used as a switch. Finally, a breaker failure diagnostic model based on the deep self-coding network is established. Pretraining and tuning and a 126\u2009kV high-voltage switch are used to simulate different types of faults and validate the method. Experimental results show that this method can acquire sample failure data and perform failure diagnosis, and the diagnosis accuracy rate reaches 97.5%. The deep self-coding network can fully pierce deep information on the switch vibration signal.<\/jats:p>","DOI":"10.1515\/pjbr-2022-0096","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T08:40:40Z","timestamp":1685090440000},"source":"Crossref","is-referenced-by-count":3,"title":["Deep auto-encoder network for mechanical fault diagnosis of high-voltage circuit breaker operating mechanism"],"prefix":"10.1515","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9537-1757","authenticated-orcid":false,"given":"Qiuping","family":"Yang","sequence":"first","affiliation":[{"name":"Xinxiang Vocational and Technical College , Xinxiang , Henan, 453006 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1715-7819","authenticated-orcid":false,"given":"Fang","family":"Hao","sequence":"additional","affiliation":[{"name":"Xinxiang Vocational and Technical College , Xinxiang , Henan, 453006 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"2025073006061525417_j_pjbr-2022-0096_ref_001","doi-asserted-by":"crossref","unstructured":"C. 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