Journal
IEEE ACCESS
Volume 6, Issue -, Pages 49755-49766Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2868430
Keywords
Condition monitoring; fault diagnosis; industry applications; machine learning
Categories
Funding
- Generalitat de Catalunya (GRC MCIA) [SGR 2014-101]
- Spanish Ministry of Economy and Competitiveness [TRA2016-80472-R]
- CONACyT Scholarship [313604]
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An industrial machinery condition monitoring methodology based on ensemble novelty detection and evolving classification is proposed in this study. The methodology contributes to solve current challenges dealing with classical electromechanical system monitoring approaches applied in industrial frameworks, that is, the presence of unknown events, the limitation to the nominal healthy condition as starting knowledge, and the incorporation of new patterns to the available knowledge. The proposed methodology is divided into four main stages: 1) a dedicated feature calculation and reduction over available physical magnitudes to increase novelty detection and fault classification capabilities; 2) a novelty detection based on the ensemble of one-class support vector machines to identify not previously considered events; 3) a diagnosis by means of eClass evolving classifiers for patterns recognition; and 4) re-training to include new patterns to the novelty detection and fault identification models. The effectiveness of the proposed fault detection and identification methodology has been compared with classical approaches, and verified by experimental results obtained from an automotive end-of-line test machine.
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