{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T01:03:14Z","timestamp":1778029394525,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52075435"],"award-info":[{"award-number":["52075435"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022JZ-30"],"award-info":[{"award-number":["2022JZ-30"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21JK0805"],"award-info":[{"award-number":["21JK0805"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["252072105"],"award-info":[{"award-number":["252072105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Program Key Project of Shaanxi Province","award":["52075435"],"award-info":[{"award-number":["52075435"]}]},{"name":"Natural Science Basic Research Program Key Project of Shaanxi Province","award":["2022JZ-30"],"award-info":[{"award-number":["2022JZ-30"]}]},{"name":"Natural Science Basic Research Program Key Project of Shaanxi Province","award":["21JK0805"],"award-info":[{"award-number":["21JK0805"]}]},{"name":"Natural Science Basic Research Program Key Project of Shaanxi Province","award":["252072105"],"award-info":[{"award-number":["252072105"]}]},{"name":"Natural Science Special Project of Education Department of Shaanxi Provincial Government","award":["52075435"],"award-info":[{"award-number":["52075435"]}]},{"name":"Natural Science Special Project of Education Department of Shaanxi Provincial Government","award":["2022JZ-30"],"award-info":[{"award-number":["2022JZ-30"]}]},{"name":"Natural Science Special Project of Education Department of Shaanxi Provincial Government","award":["21JK0805"],"award-info":[{"award-number":["21JK0805"]}]},{"name":"Natural Science Special Project of Education Department of Shaanxi Provincial Government","award":["252072105"],"award-info":[{"award-number":["252072105"]}]},{"name":"Doctoral Dissertation Innovation Fund of Xi\u2019an University of Technology","award":["52075435"],"award-info":[{"award-number":["52075435"]}]},{"name":"Doctoral Dissertation Innovation Fund of Xi\u2019an University of Technology","award":["2022JZ-30"],"award-info":[{"award-number":["2022JZ-30"]}]},{"name":"Doctoral Dissertation Innovation Fund of Xi\u2019an University of Technology","award":["21JK0805"],"award-info":[{"award-number":["21JK0805"]}]},{"name":"Doctoral Dissertation Innovation Fund of Xi\u2019an University of Technology","award":["252072105"],"award-info":[{"award-number":["252072105"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Data-driven fault diagnosis methods for rotating machinery have developed rapidly with the help of deep learning methods. However, traditional intelligent fault diagnosis methods still have some limitations in fault feature extraction and the latest object detection theory has not been applied in fault diagnosis. To this end, a fault diagnosis method based on a sparse short-term Fourier transform (SSTFT) and object detection theory is developed in this paper. First, a sparse constraint is introduced in time-frequency analysis to improve the time-frequency resolution of the model without cross-term interference and proximal gradient descent (PGD) is adopted to quickly and effectively optimize the model to obtain a high-quality time-frequency representation (TFR). Second, a fault diagnosis model based on a region-based convolutional neural network (RCNN) is built; the model can extract multiple regions that can characterize fault features from the TFR. This process avoids the interference of irrelevant vibration components and improves the interpretability of the fault diagnosis model. Finally, multicategory rolling bearing fault identification is realized. The effectiveness of the proposed method is validated by simulation signals and bearing experiments. The results indicate that the proposed method is more effective than existing methods.<\/jats:p>","DOI":"10.3390\/e24121822","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T04:54:49Z","timestamp":1670993689000},"page":"1822","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Rolling Bearing Fault Monitoring for Sparse Time-Frequency Representation and Feature Detection Strategy"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4933-527X","authenticated-orcid":false,"given":"Jiahui","family":"Tang","sequence":"first","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Jimei","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"Faculty of Printing, Packing and Digital Media Engineering, Xi\u2019an University of Technology, Xi\u2019an 710054, China"}]},{"given":"Jiajuan","family":"Qing","sequence":"additional","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Tuo","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106587","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of machine learning to machine fault diagnosis: A review and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. 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