Evaluation of multiclass novelty detection algorithms for electric machine monitoring
08864874.pdf (1,539Mb) (Restricted access) Request copy
Què és aquest botó?
Aquest botó permet demanar una còpia d'un document restringit a l'autor. Es mostra quan:
- Disposem del correu electrònic de l'autor
- El document té una mida inferior a 20 Mb
- Es tracta d'un document d'accés restringit per decisió de l'autor o d'un document d'accés restringit per política de l'editorial
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
The detection of unexpected events represents, currently, one of the most critical challenges dealing with electromechanical system diagnosis. In this regard, machine learning based algorithms widely applied in other fields of application are being considered now to face the novelty detection during the electric machine monitoring. In this study, an electrical monitoring scheme is considered for novelty detection performance evaluation, where vibration signals under different bearing fault conditions are acquired. Thus, the common electric machine monitoring framework, that is, a set of features estimated from a limited number of measurements, is considered in front of the three main novelty detection approaches: probability, domain and distance based. Performance of the corresponding approaches are studied and discussed experimentally. It is revealed that, although novelty detection provides enhanced diagnosis results in all cases, the response of some approaches fit better with the patterns resulting from the electric machine faults and the characteristics of the available measurements.
CitationRamirez, M. [et al.]. Evaluation of multiclass novelty detection algorithms for electric machine monitoring. A: IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. "SDEMPED - 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED): 27-30 August 2019: Toulouse, France". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 330-337.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder