Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93436
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical Engineering | en_US |
dc.creator | Wu, J | en_US |
dc.creator | Wu, C | en_US |
dc.creator | Cao, S | en_US |
dc.creator | Or, SW | en_US |
dc.creator | Deng, C | en_US |
dc.creator | Shao, X | en_US |
dc.date.accessioned | 2022-06-21T08:23:45Z | - |
dc.date.available | 2022-06-21T08:23:45Z | - |
dc.identifier.issn | 0278-0046 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93436 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication J. Wu, C. Wu, S. Cao, S. W. Or, C. Deng and X. Shao, "Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines," in IEEE Transactions on Industrial Electronics, vol. 66, no. 1, pp. 529-539, Jan. 2019 is available at https://doi.org/10.1109/TIE.2018.2811366 | en_US |
dc.subject | Degradation data-driven approach | en_US |
dc.subject | Degradation feature | en_US |
dc.subject | Electrical machines | en_US |
dc.subject | Rolling element bearings (REBs) | en_US |
dc.subject | Time-to-failure (TTF) prognostics | en_US |
dc.title | Degradation data-driven time-to-failure prognostics approach for rolling element bearings in electrical machines | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 529 | en_US |
dc.identifier.epage | 539 | en_US |
dc.identifier.volume | 66 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.1109/TIE.2018.2811366 | en_US |
dcterms.abstract | Time-to-failure (TTF) prognostic plays a crucial role in predicting remaining lifetime of electrical machines for improving machinery health management. This paper presents a novel three-step degradation data-driven TTF prognostics approach for rolling element bearings (REBs) in electrical machines. In the degradation feature extraction step, multiple degradation features, including statistical features, intrinsic energy features, and fault frequency features, are extracted to detect the degradation phenomenon of REBs using complete ensemble empirical mode decomposition with adaptive noise and Hilbert-Huang transform methods. In degradation feature reduction step, the degradation features, which are monotonic, robust, and correlative to the fault evolution of the REBs, are selected and fused into a principal component Mahalanobis distance health index using dynamic principal component analysis and Mahalanobis distance methods. In TTF prediction step, the degradation process and local TTF of the REBs are observed by an exponential regression-based local degradation model, and the global TTF is predicted by an empirical Bayesian algorithm with a continuous update. A practical case study involving run-to-failure experiments of REBs on PRONOSTIA platform is provided to validate the effectiveness of the proposed approach and to show a more accurate prediction of TTF than the existing major approaches. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on industrial electronics, Jan. 2019, v. 66, no. 1, p. 529-539 | en_US |
dcterms.isPartOf | IEEE transactions on industrial electronics | en_US |
dcterms.issued | 2019-01 | - |
dc.identifier.scopus | 2-s2.0-85042856837 | - |
dc.identifier.eissn | 1557-9948 | en_US |
dc.description.validate | 202206 bchy | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EE-0266 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Foundation of the National Key Intergovernmental Special Project Development Plan of China; Fundamental Research Funds for the Central Universities; Innovation and Technology Commission of the HKSAR Goverment to the Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6824836 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Or_Degradation_Data-Driven_Time-To-Failure.pdf | Pre-Published version | 1.89 MB | Adobe PDF | View/Open |
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