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Compound fault diagnosis for a rolling bearing using adaptive DTCWPT with higher order spectra
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.ORCID iD: 0000-0002-8018-1774
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-7458-6820
Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China.
ZhenDui Industry Artificial Intelligence Co., Ltd, Shenzhen, China.
2020 (English)In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 32, no 3, p. 342-353Article in journal (Refereed) Published
Abstract [en]

Fault diagnosis plays a vital role in prognostics and health management. Researchers have devoted their efforts in enhancing the accuracy of fault diagnosis. However, diagnosis of compound faults in complex systems is still a challenging task. The problem lies in the coupling of multiple signals, which may conceal the characteristics of compound faults. Taking a rolling bearing as an example, this study aims to boost the accuracy of compound fault diagnosis through a novel feature extraction approach to making the fault characteristics more discriminative. The approach proposes an adaptive dual-tree complex wavelet packet transform (DTCWPT) with higher order spectra analysis. To flexibly and best match the characteristics of the measured vibration signals under analysis, DTCWPT is first adaptively determined by the minimum singular value decomposition entropy. Then, higher order spectra analysis is performed on the decomposed frequency sensitive band for feature extraction and enhancement. The proposed approach is used to analyze experimental signals of a bearing’s compound faults and found effective.

Place, publisher, year, edition, pages
Taylor & Francis, 2020. Vol. 32, no 3, p. 342-353
Keywords [en]
Adaptive dual-tree complex wavelet packet, compound faults, prognostics and health management, rolling bearing, singular value decomposition
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-78978DOI: 10.1080/08982112.2020.1749654ISI: 000536343700001Scopus ID: 2-s2.0-85085294687OAI: oai:DiVA.org:ltu-78978DiVA, id: diva2:1431915
Note

Validerad;2020;Nivå 2;2020-08-18 (johcin)

Available from: 2020-05-25 Created: 2020-05-25 Last updated: 2022-10-28Bibliographically approved

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Shao, HaidongLin, Jing

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