Novelty detection based condition monitoring scheme applied to electromechanical systems
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
This study is focused on the current challenges dealing with electromechanical system monitoring applied in industrial frameworks, that is, the presence of unknown events and the limitation to the nominal healthy condition as starting knowledge. Thus, an industrial machinery condition monitoring methodology based on novelty detection and classification is proposed in this study. The methodology is divided in three main stages. First, a dedicated feature calculation and reduction over each available physical magnitude. Second, an ensemble structure of novelty detection models based on one-class support vector machines to identify not previously considered events. Third, a diagnosis model supported by a feature fusion scheme in order to reach high fault classification capabilities. The effectiveness of the fault detection and identification methodology has been compared with classical single model approach, and verified by experimental results obtained from an electromechanical machine. © 2018 IEEE.
CitationDelgado Prieto, M., Cariño, J. A., Saucedo, J., Osornio, R., Romeral, L., Romero, R. Novelty detection based condition monitoring scheme applied to electromechanical systems. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA): Politecnico di Torino, Torino, Italy, 04-07 September, 2018: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1213-1216.
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