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This method uses DLMD to decompose the current signal and vibration signal, respectively, and weights the decomposed product function (PF) according to the kurtosis value to reconstruct the signal, and then fuses the reconstructed signals to obtain the current\u2013vibration fusion signal after normalization, and then analyzes the fusion signal spectrally through the Hilbert envelope spectrum. Finally, the fusion signal is analyzed by the Hilbert envelope spectrum, and a clear fault characteristic frequency is obtained. The experimental results demonstrate that compared to traditional bearing fault diagnosis methods, the proposed method significantly improves the signal-to-noise ratio of fault signals, effectively enhances the sensitivity of early-stage fault detection in motor bearings, and improves the accuracy of fault identification.<\/jats:p>","DOI":"10.3390\/s24113373","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T06:59:04Z","timestamp":1716533944000},"page":"3373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Early-Stage Fault Diagnosis of Motor Bearing Based on Kurtosis Weighting and Fusion of Current\u2013Vibration Signals"],"prefix":"10.3390","volume":"24","author":[{"given":"Bingye","family":"Zhang","sequence":"first","affiliation":[{"name":"State Grid Taizhou Power Company, Taizhou 318000, China"}]},{"given":"Haibo","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Taizhou Power Company, Taizhou 318000, China"}]},{"given":"Weiyi","family":"Kong","sequence":"additional","affiliation":[{"name":"State Grid Taizhou Power Company, Taizhou 318000, China"}]},{"given":"Minjie","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, Hangzhou 310007, China"}]},{"given":"Jien","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, Hangzhou 310007, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1109\/TIE.2014.2345330","article-title":"Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current","volume":"62","author":"Leite","year":"2015","journal-title":"IEEE Trans. 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