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Unsupervised Deep Learning for Induction Motor Bearings Monitoring

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Data-Enabled Discovery and Applications

Abstract

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 motor 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.

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Authors and Affiliations

  1. DIBRIS, University of Genova, Via Opera Pia 13, 16145, Genova, Italy

    Francesca Cipollini & Luca Oneto

  2. Department of Naval Architecture, Ocean, Marine Engineering, University of Strathclyde, 100 Montrose Street, Glasgow, G4 0LZ, UK

    Andrea Coraddu

  3. DITEN, University of Genova, Via Opera Pia 13, 16145, Genova, Italy

    Stefano Savio

Authors
  1. Francesca Cipollini
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  2. Luca Oneto
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  3. Andrea Coraddu
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  4. Stefano Savio
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Correspondence to Luca Oneto.

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Cipollini, F., Oneto, L., Coraddu, A. et al. Unsupervised Deep Learning for Induction Motor Bearings Monitoring. Data-Enabled Discov. Appl. 3, 1 (2019). https://doi.org/10.1007/s41688-018-0025-2

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