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

Romain Breuneval; Guy Clerc; Babak Nahid-Mobarakeh; Badr Mansouri; Romain Breuneval; Guy Clerc; Babak Nahid-Mobarakeh; Badr Mansouri

doi:10.3934/ElectrEng.2018.3.59

AIMS Electronics and Electrical Engineering

2018, Volume 2, Issue 3: 59-84. doi: 10.3934/ElectrEng.2018.3.59

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Research article Topical Sections

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

Received: 28 May 2018 Accepted: 05 September 2018 Published: 13 September 2018

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.
- health-monitoring,
- diagnosis,
- SVM,
- fuzzy membership function,
- information fusion,
- triangular norm,
- unknown classes,
- rejection,
- electromechanical actuator,
- reject option
Citation: Romain Breuneval, Guy Clerc, Babak Nahid-Mobarakeh, Badr Mansouri. Classification with automatic detection of unknown classes based on SVM and fuzzy MBF: Application to motor diagnosis[J]. AIMS Electronics and Electrical Engineering, 2018, 2(3): 59-84. doi: 10.3934/ElectrEng.2018.3.59

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Abstract

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.

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