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Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring
Qatar University, Qatar.ORCID iD: 0000-0003-0530-9552
Qatar University, Qatar.
Qatar University, Qatar.
Qatar University, Qatar.
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2019 (English)In: IEEE transactions on industrial electronics (1982. Print), ISSN 0278-0046, E-ISSN 1557-9948, Vol. 66, no 10, p. 8136-8147Article in journal (Refereed) Published
Abstract [en]

This paper presents a fast, accurate, and simple systematic approach for online condition monitoring and severity identification of ball bearings. This approach utilizes compact one-dimensional (1-D) convolutional neural networks (CNNs) to identify, quantify, and localize bearing damage. The proposed approach is verified experimentally under several single and multiple damage scenarios. The experimental results demonstrated that the proposed approach can achieve a high level of accuracy for damage detection, localization, and quantification. Besides its real-time processing ability and superior robustness against the high-level noise presence, the compact and minimally trained 1-D CNNs in the core of the proposed approach can handle new damage scenarios with utmost accuracy.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 66, no 10, p. 8136-8147
National Category
Reliability and Maintenance
Research subject
Technology (byts ev till Engineering), Industrial economy
Identifiers
URN: urn:nbn:se:lnu:diva-88123DOI: 10.1109/TIE.2018.2886789OAI: oai:DiVA.org:lnu-88123DiVA, id: diva2:1344136
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-08-28Bibliographically approved

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Abdeljaber, Osama
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