{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T17:37:27Z","timestamp":1761845847022,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Joint Funds of the National Natural Science Foundation of China","award":["No. U2267206"],"award-info":[{"award-number":["No. U2267206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the reconstructed envelope spectrum is proposed to improve the ability of rolling bearing cross-domain fault diagnostics in this paper. First, based on the envelope spectrum morphology of rolling bearing failures, a standard envelope spectrum is constructed that reveals the unique characteristics of different bearing health states and eliminates the differences between domains due to different bearing speeds and bearing models. Then, a fault diagnosis model was constructed using a convolutional neural network to learn features and complete fault classification. Finally, using two publicly available bearing data sets and one bearing data set obtained by self-experimentation, the proposed method is applied to the data of the fault diagnostics of rolling bearings under different rotational speeds and different bearing types. The experimental results show that, compared with some popular feature extraction methods, the proposed method can achieve high diagnostic accuracy with data at different rotational speeds and different bearing types, and it is an effective method for solving the problem with cross-domain fault diagnostics for rolling bearings.<\/jats:p>","DOI":"10.3390\/s24113500","type":"journal-article","created":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T06:58:07Z","timestamp":1716965887000},"page":"3500","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Deep Learning Method for Bearing Cross-Domain Fault Diagnostics Based on the Standard Envelope Spectrum"],"prefix":"10.3390","volume":"24","author":[{"given":"Lubin","family":"Zhai","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Xiufeng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Zeyiwen","family":"Si","sequence":"additional","affiliation":[{"name":"School of Mathematics, University of Bristol, Bristol BS8 1QU, UK"}]},{"given":"Zedong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112879","DOI":"10.1016\/j.measurement.2023.112879","article-title":"A new bearing fault diagnosis method via simulation data driving transfer learning without target fault data","volume":"215","author":"Hou","year":"2023","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.measurement.2018.04.063","article-title":"Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis","volume":"124","author":"Li","year":"2018","journal-title":"Measurement"},{"key":"ref_3","first-page":"5067651","article-title":"Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings","volume":"2017","author":"Verstraete","year":"2017","journal-title":"Shock Vib."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1016\/j.ymssp.2007.02.003","article-title":"Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine","volume":"21","author":"Abbasion","year":"2007","journal-title":"Mech. 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