{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:29:09Z","timestamp":1780554549399,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T00:00:00Z","timestamp":1696032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China National Key Programs of Science and Technology","award":["GH-2-102"],"award-info":[{"award-number":["GH-2-102"]}]},{"name":"China National Key Programs of Science and Technology","award":["GH-084"],"award-info":[{"award-number":["GH-084"]}]},{"name":"China National Key Programs of Science and Technology","award":["GH-2-102"],"award-info":[{"award-number":["GH-2-102"]}]},{"name":"China National Key Programs of Science and Technology","award":["GH-084"],"award-info":[{"award-number":["GH-084"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Three frequently encountered problems\u2014a variety of fault types, data with insufficient labels, and missing fault types\u2014are the common challenges in the early fault diagnosis of space flywheel rotor systems. Focusing on the above issues, this paper proposes an intelligent early fault diagnosis method based on the multi-channel convolutional neural network with hierarchical branch and similarity clustering (HB-SC-MCCNN). First, a similarity clustering (SC) method is integrated into the parameter-shared dual MCCNN architecture to set up as the basic structural block. The hierarchical branch model and additional loss are then added to SC-MCCNN to form a hierarchical branch network, which simplifies the problem of fault multi-classification into binary classification with multi-steps. Based on the self-learning characteristics of the proposed model, the unlabeled data and the missing fault types in the training set are re-labeled to realize the re-training of the network. The results of the experiments for comparing the abilities between the proposed method and several advanced deep learning models confirm that on the established early fault dataset of the space flywheel rotor system, the proposed method successfully achieves the hierarchical diagnosis and presents stronger competitiveness in the case of insufficient labeled data and missing fault types at the same time.<\/jats:p>","DOI":"10.3390\/s23198198","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T04:39:30Z","timestamp":1696221570000},"page":"8198","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Intelligent Early Fault Diagnosis of Space Flywheel Rotor System"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6363-8947","authenticated-orcid":false,"given":"Hui","family":"Liao","sequence":"first","affiliation":[{"name":"School of Mechatronics Engineering, Northwestern Polytechnical University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China"},{"name":"Luoyang Bearing Research Institute Co., Ltd., Luoyang 471039, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sier","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Northwestern Polytechnical University, Xi\u2019an 710071, China"},{"name":"School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengdi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3504313","DOI":"10.1109\/TIM.2020.3033061","article-title":"Time-Varying Envelope Filtering for Exhibiting Space Bearing Cage Fault Features","volume":"70","author":"Wei","year":"2021","journal-title":"IEEE Trans. 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