{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T18:01:58Z","timestamp":1781200918917,"version":"3.54.1"},"reference-count":50,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Engineering Research Center of Key Technology for Intelligent Manufacturing Equipment","award":["M202108"],"award-info":[{"award-number":["M202108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating an adaptive multi-bandpass filter (AMBPF) and refined composite multi-scale fuzzy entropy (RCMFE). And a dream optimization algorithm (DOA)\u2013least squares support vector machine (LSSVM) is also proposed for fault classification. Firstly, the AMBPF is proposed, which can effectively and adaptively separate the meshing frequencies, harmonic frequencies, and their sideband frequency information of the planetary gearbox, and is combined with RCMFE for fault feature extraction. Secondly, the DOA is employed to optimize the parameters of the LSSVM, aiming to enhance its classification efficiency. Finally, the fault diagnosis of the planetary gearbox is achieved by the AMBPF, RCMFE, and DOA-LSSVM. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic efficiency and exhibits superior noise immunity in planetary gearbox fault diagnosis.<\/jats:p>","DOI":"10.3390\/sym17081179","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T16:04:51Z","timestamp":1753286691000},"page":"1179","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Fault Diagnosis Method for Planetary Gearboxes Using an Adaptive Multi-Bandpass Filter, RCMFE, and DOA-LSSVM"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2259-7982","authenticated-orcid":false,"given":"Xin","family":"Xia","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Suqian University, Suqian 223800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4725-423X","authenticated-orcid":false,"given":"Aiguo","family":"Wang","sequence":"additional","affiliation":[{"name":"Information Construction Center, Suqian University, Suqian 223800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyu","family":"Sun","sequence":"additional","affiliation":[{"name":"Information Construction Center, Suqian University, Suqian 223800, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"012002","DOI":"10.1088\/1361-6501\/acf390","article-title":"A review on deep learning in planetary gearbox health state recognition: Methods, applications, and dataset publication","volume":"35","author":"Liu","year":"2023","journal-title":"Meas. 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