Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Classification with automatic detection of unknown classes based on SVM and fuzzy MBF: Application to motor diagnosis

1 Safran Electronics & Defense, Avionics Division, Center of Product Excellence in Avionic Systems and Actuation (CEP-SAA) F-91300, Massy, France
2 University of Lyon, UCB Lyon 1, CNRS, AMPERE, F-69100, Villeurbanne, France
3 Groupe de Recherche en Electrotechnique et Electronique de Nancy (GREEN), University of Lorraine, F-54510 Nancy, France

Topical Section: Artificial Intelligence and Machine Learning

Classification algorithms based on data mining tools show good performances for the automatic diagnosis of systems. However, these performances degrade quickly when the database is not exhaustive. This happens, for example, when a new class appears. This class could correspond to a previous unknown fault or to an unknown combination of simultaneous faults. Described algorithm in this paper proposes a solution to this issue. It combines Support Vector Machine (SVM), fuzzy membership functions (mbf) and fuzzy information fusion. It results in the construction of a matrix of memberships to known classes U_class and a vector of membership to unknown classes U_others. Then, from these values, indicators of distance and ambiguity of the observations can be computed. These indicators allow setting a simple rejection rule with a threshold classifier. The algorithm is validated by using Cross-Validation (CV) on experimental data on an induction motor faults supplied by a voltage-source inverter. The results show the good performances of the proposed algorithms and its suitability for transportation systems like aircrafts.
  Article Metrics
Download full text in PDF

Export Citation

Article outline

Copyright © AIMS Press All Rights Reserved