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Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition
Department of Informatics and Telecommunications Technology, Technological Educational Institute of Epirus.ORCID iD: 0000-0001-9701-4203
bDepartment of Mechanical Engineering and Aeronautics, University of Patras.
Department of Informatics and Telecommunications Technology, Technological Educational Institute of Epirus.
Department of Mechanical Engineering and Aeronautics, University of Patras.
2013 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 41, no 1-2, p. 510-525Article in journal (Refereed) Published
Abstract [en]

Aiming at more efficient fault diagnosis, this research work presents an integrated anomaly detection approach for seeded bearing faults. Vibration signals from normal bearings and bearings with three different fault locations, as well as different fault sizes and loading conditions are examined. The Empirical Mode Decomposition and the Hilbert Huang transform are employed for the extraction of a compact feature set. Then, a hybrid ensemble detector is trained using data coming only from the normal bearings and it is successfully applied for the detection of any deviation from the normal condition. The results prove the potential use of the proposed scheme as a first stage of an alarm signalling system for the detection of bearing faults irrespective of their loading condition.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 41, no 1-2, p. 510-525
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-68289DOI: 10.1016/j.ymssp.2013.02.020Scopus ID: 2-s2.0-84885579397OAI: oai:DiVA.org:ltu-68289DiVA, id: diva2:1196728
Available from: 2018-04-11 Created: 2018-04-11 Last updated: 2018-04-11Bibliographically approved

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