{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:07:10Z","timestamp":1760058430281,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nantong Institute of Technology electronic information master\u2019s project","award":["879002","2024-AFCEC-088","2024JGG015"],"award-info":[{"award-number":["879002","2024-AFCEC-088","2024JGG015"]}]},{"name":"National College Computer Basic Education Teaching Research Project","award":["879002","2024-AFCEC-088","2024JGG015"],"award-info":[{"award-number":["879002","2024-AFCEC-088","2024JGG015"]}]},{"name":"Nantong Institute of Technology University-level \u201cPublic Course Education Teaching Reform Research\u201d project","award":["879002","2024-AFCEC-088","2024JGG015"],"award-info":[{"award-number":["879002","2024-AFCEC-088","2024JGG015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In this paper, a reliability assessment and prediction method based on bearing vibration signals is proposed, which combines Adaptive Cyclostationary Blind Deconvolution (ACYCBD) and AdaBoost-Mixed Kernel Relevance Vector Machine. Firstly, CYCBD parameters were optimized by the Ivy optimization algorithm to enhance the noise reduction effect, and then multidimensional features were extracted and dimensionalization was reduced by PaCMAP. Based on dimensionality reduction features, logistic regression was used to evaluate reliability, and AdaBoost-MKRVM was combined to predict reliability. The experimental results show that the mean absolute error (MAE) of the proposed method on the bearing life dataset of Xi\u2019an Jiaotong University is 0.052, which is better than the traditional method, and provides a new idea for the performance prediction of rolling bearings.<\/jats:p>","DOI":"10.3390\/a18040192","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T13:36:49Z","timestamp":1743169009000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Intelligent Reliability Assessment and Prognosis of Rolling Bearings Using Adaptive Cyclostationary Blind Deconvolution and AdaBoost-Mixed Kernel Relevance Vector Machine"],"prefix":"10.3390","volume":"18","author":[{"given":"Yifan","family":"Yu","sequence":"first","affiliation":[{"name":"Yonyou Digital Intelligence Academy, Nantong Institute of Technology, Nantong 226001, China"}]},{"given":"Shuxi","family":"Chen","sequence":"additional","affiliation":[{"name":"Yonyou Digital Intelligence Academy, Nantong Institute of Technology, Nantong 226001, China"}]},{"given":"Depeng","family":"Gao","sequence":"additional","affiliation":[{"name":"Yonyou Digital Intelligence Academy, Nantong Institute of Technology, Nantong 226001, China"}]},{"given":"Jianlin","family":"Qiu","sequence":"additional","affiliation":[{"name":"Yonyou Digital Intelligence Academy, Nantong Institute of Technology, Nantong 226001, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1007\/s11668-024-01899-6","article-title":"Wind Turbine Gearbox Bearing Fault Diagnosis Method Based on ICEEMDAN and Flexible Wavelet Threshold","volume":"24","author":"Gao","year":"2024","journal-title":"J. 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