Early Detection of Faults in Induction Motors—A Review

Tomas Garcia-Calva, Daniel Morinigo-Sotelo, Vanessa Fernandez-Cavero and Rene Romero-Troncoso ()
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Tomas Garcia-Calva: HSPdigital-Electronics Department, University of Guanajuato, Salamanca 36700, Mexico
Daniel Morinigo-Sotelo: HSPdigital-ITAP-ADIRE, University of Valladolid, 47002 Valladolid, Spain
Vanessa Fernandez-Cavero: Department of Electrical Engineering, University of Valladolid, 47002 Valladolid, Spain
Rene Romero-Troncoso: HSPdigital-Mechatronics Department, Autonomous University of Querétaro, San Juan del Río 76806, Mexico

Energies, 2022, vol. 15, issue 21, 1-18

Abstract: There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented.

Keywords: artificial intelligence; condition monitoring; early detection; fault diagnosis; fault severity; frequency analysis; incipient fault; induction motor; machine learning; signal processing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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