Please use this identifier to cite or link to this item: https://doi.org/10.1109/TII.2018.2869429
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dc.titleJoint Online RUL Prediction for Multivariate Deteriorating Systems
dc.contributor.authorPENG WEIWEN
dc.contributor.authorYE ZHISHENG
dc.contributor.authorCHEN NAN
dc.date.accessioned2020-05-21T07:51:00Z
dc.date.available2020-05-21T07:51:00Z
dc.date.issued2018-09-11
dc.identifier.citationPENG WEIWEN, YE ZHISHENG, CHEN NAN (2018-09-11). Joint Online RUL Prediction for Multivariate Deteriorating Systems. IEEE Transactions on Industrial Informatics 15 (5) : 2870-2878. ScholarBank@NUS Repository. https://doi.org/10.1109/TII.2018.2869429
dc.identifier.issn15513203
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/168383
dc.description.abstractStochastic processes and filtering methods are popular tools for degradation modeling and online remaining useful life (RUL) prediction. However, most models are for one-dimensional degradation and various filtering methods can only handle observations from a single system. This paper studies joint online RUL prediction of multi deteriorating systems with multi system observations and measurement errors. A multivariate degradation model equipped with a batch particle filter is developed and built for characterizing multiple dependent performance deteriorations with measurement errors in each system. The batch particle filter is developed for simultaneous online parameter estimation and degradation state identification by leveraging multi system observations. A numerical example and a case study are provided to demonstrate the proposed method. The results show that homogeneous multi system observations from a population of multi deteriorating systems can be jointly processed on-the-fly. Individualized online RUL prediction with improved precision for each system can be achieved through the joint online inference.
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/8458202
dc.publisherIEEE Computer Society
dc.subjectDegradation modeling
dc.subjectOnline inference
dc.subjectParticle filter
dc.subjectPrognostics and health management (PHM)
dc.subjectRemaining useful life (RUL)
dc.typeArticle
dc.contributor.departmentDEPT OF INDUSTRIAL SYSTEMS ENGG & MGT
dc.description.doi10.1109/TII.2018.2869429
dc.description.sourcetitleIEEE Transactions on Industrial Informatics
dc.description.volume15
dc.description.issue5
dc.description.page2870-2878
dc.published.statePublished
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