{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:16:00Z","timestamp":1740147360910,"version":"3.37.3"},"reference-count":28,"publisher":"Wiley","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Fund of China","award":["U1604158","2012HASTIT005"],"award-info":[{"award-number":["U1604158","2012HASTIT005"]}]},{"name":"Technical Innovation Talents Scheme of Henan Province","award":["U1604158","2012HASTIT005"],"award-info":[{"award-number":["U1604158","2012HASTIT005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Control Science and Engineering"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Residual useful life (RUL) prediction is significant for condition-based maintenance. Traditional data-driven RUL prediction method can only predict fault trend of the system rather than RUL of a specific system component. Thus it cannot tell the operator which component should be maintained. The innovation of this paper is as follows: (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mn fontstyle=\"italic\">1<\/mml:mn><\/mml:mrow><\/mml:math>) Wavelet filtering based method is developed for early detection of slowly varying fault. (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:mn fontstyle=\"italic\">2<\/mml:mn><\/mml:mrow><\/mml:math>) Designated component analysis is introduced as a feature extraction tool to define the fault precursor of a specific component. (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mrow><mml:mn fontstyle=\"italic\">3<\/mml:mn><\/mml:mrow><\/mml:math>) Exponential life prediction model is established by nonlinear fitting of the historical RUL and the fault size characterized by the statistics used. Once online detection statistics is obtained, real-time RUL of the critical component can be predicted online. Simulation shows the effectiveness of this algorithm.<\/jats:p>","DOI":"10.1155\/2017\/8492139","type":"journal-article","created":{"date-parts":[[2017,5,21]],"date-time":"2017-05-21T17:01:31Z","timestamp":1495386091000},"page":"1-11","source":"Crossref","is-referenced-by-count":0,"title":["DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3592-9664","authenticated-orcid":true,"given":"Funa","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6910-0902","authenticated-orcid":true,"given":"Jiayu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng, 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