Unintrusive monitoring of induction motors bearings via deep learning on stator currents

Francesca Cipollini, Luca Oneto, Andrea Coraddu, Stefano Savio, Davide Anguita

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)
24 Downloads (Pure)


Induction motors are fundamental components of several modern automation system, and they are one of the central pivot of the developing e-mobility era. The most vulnerable parts of an induction motor are the bearings, the stator winding and the rotor bars. Consequently, monitoring and maintaining them during operations is vital. In this work, authors propose an Induction Motors bearings monitoring tool which leverages on stator currents signals processed with a Deep Learning architecture. Differently from the state-of-the-art approaches which exploit vibration signals, collected by easily damageable and intrusive vibration probes, the stator currents signals are already commonly available, or easily and unintrusively collectable. Moreover, instead of using now-classical data-driven models, authors exploit a Deep Learning architecture able to extract from the stator current signal a compact and expressive representation of the bearings state, ultimately providing a bearing fault detection system. In order to estimate the effectiveness of the proposal, authors collected a series of data from an inverter-fed motor mounting different artificially damaged bearings. Results show that the proposed approach provides a promising and effective yet simple bearing fault detection system.
Original languageEnglish
Pages (from-to)42-51
Number of pages10
JournalProcedia Computer Science
Early online date21 Nov 2018
Publication statusE-pub ahead of print - 21 Nov 2018
Event3rd International Neural Network Society Conference on Big Data and Deep Learning, INNS BDDL 2018 - Sanur, Bali, Indonesia
Duration: 17 Apr 201819 Apr 2018


  • bearings
  • deep learning
  • induction motors
  • monitoring
  • stator currents


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