Multi-Sensor Fault Diagnosis of Induction Motors Using Random Forests and Support Vector Machine

Alireza Nemat Saberi, Sarvavignoban Sandirasegaram, Anouar Belahcen, Toomas Vaimann, Jan Sobra

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
87 Downloads (Pure)


This paper presents a fault diagnosis scheme for induction machines (IMs) using Support Vector Machine (SVM) and Random Forests (RFs). First, a number of time domain and frequency-domain features are extracted from vibration and current signals in different operating conditions of IM. Then, these features are combined and considered as the input of SVM-based classification model. To avoid overfitting, RF is utilized to determine the most dominant features contributing to accurate classification. It is proved that the proposed method is capable of achieving highly accurate fault diagnosis results for broken rotor bar and eccentricity faults and it can appropriately handle the high dimensionality of the combined data.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Electrical Machines, ICEM 2020
Number of pages7
ISBN (Electronic)9781728199450
ISBN (Print)978-1-7281-9945-0
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Electrical Machines - Virtual, Online
Duration: 23 Aug 202026 Aug 2020
Conference number: 25

Publication series

NameProceedings (International Conference on Electrical Machines)
ISSN (Print)2381-4802
ISSN (Electronic)2473-2087


ConferenceInternational Conference on Electrical Machines
Abbreviated titleICEM
CityVirtual, Online
OtherVirtual Conference


  • Fault diagnosis
  • Induction motors
  • Machine learning
  • Multible signal classification
  • Support vector machine


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