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Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor
The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China.
The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China.
The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-7458-6820
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 37611-37619Article in journal (Refereed) Published
Abstract [en]

The texture feature tensor established from a subband time–frequency image (TFI) was extracted and used to identify the fault states of a rolling bearing. The TFI of adaptive optimal-kernel distribution was optimally partitioned into TFI blocks based on the minimum frequency band entropy. The texture features were extracted from the co-occurrence matrix of each TFI block. Based on the order of the segmented frequency bands, the texture feature tensor was constructed using the multidimensional feature vectors from all the blocks; this preserved the inherent characteristic of the TFI structure and avoided the information loss caused by vectorizing multidimensional features. The linear support higher order tensor machine based on the feature tensor was applied to identify the fault states of the rolling bearing.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 7, p. 37611-37619
Keywords [en]
Texture feature tensor, frequency band entropy, linear support higher-order tensor machine, bearing fault intelligent diagnosis.
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-73640DOI: 10.1109/ACCESS.2019.2902344ISI: 000463639600001OAI: oai:DiVA.org:ltu-73640DiVA, id: diva2:1304830
Note

Validerad;2019;Nivå 2;2019-04-15 (svasva)

Available from: 2019-04-14 Created: 2019-04-14 Last updated: 2019-04-17Bibliographically approved

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Publisher's full texthttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8654647

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Lin, Jing

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  • apa
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