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Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
School of Aeronautics, Northwestern Polytechnical University, Xi’an, China.
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
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2020 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 188, article id 105022Article in journal (Refereed) Published
Abstract [en]

Early fault prognosis of bearing is a very meaningful yet challenging task to improve the security of rotating machinery. For this purpose, a novel method based on enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy is proposed in this paper. First, complex wavelet packet energy moment entropy is defined as a new monitoring index to characterize bearing performance degradation. Second, deep gated recurrent unit network is constructed to capture the nonlinear mapping relationship hidden in the defined monitoring index. Finally, a modified training algorithm based on learning rate decay strategy is developed to enhance the prognosis capability of the constructed deep model. The proposed method is applied to analyze the simulated and experimental signals of bearing. The results demonstrate that the proposed method is more superior in sensibility and accuracy to the existing methods.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 188, article id 105022
Keywords [en]
Enhanced deep gated recurrent unit, Bearing, Early fault prognosis, Energy moment entropy, Modified training algorithm
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-76058DOI: 10.1016/j.knosys.2019.105022ISI: 000513295000018OAI: oai:DiVA.org:ltu-76058DiVA, id: diva2:1352512
Note

Validerad;2020;Nivå 2;2020-01-28 (johcin)

Available from: 2019-09-19 Created: 2019-09-19 Last updated: 2020-03-13Bibliographically approved

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Haidong, Shao

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