Reliable methodology for online fault diagnosis in induction motors using passive infrared thermography

Induction motors are the mostly used electric machines in industry, due to their manufacturing features, which makes them more economical and robust. Consequently, it is of special interest to identify the most common faults present in these machines and develop reliable and cost-effective diagnosti...

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Bibliographic Details
Published in2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) pp. 600 - 607
Main Authors Redon, P., Romero-Troncoso, Rene J., Picazo-Rodenas, M. J., Antonino-Daviu, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2017
Subjects
bearing failures
failure analysis
Induction motors
infrared thermography
Monitoring
Reliability
signal processing
Temperature distribution
Temperature measurement
Temperature sensors
Ventilation
ventilation failures
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Summary:Induction motors are the mostly used electric machines in industry, due to their manufacturing features, which makes them more economical and robust. Consequently, it is of special interest to identify the most common faults present in these machines and develop reliable and cost-effective diagnostic tools to properly maintain them. In this context, the infrared thermography coupled with signal processing algorithms can be a very relevant tool. The main objective is to analyze its potential by focusing on two common fault conditions, bearing and ventilation system. To do so, the two of them were induced in an induction motor and compare during transient state and under similar working conditions against the healthy motor. The results reveal its capacity to identify the thermal sensitive regions for each case, highlight significant differences between the tested scenarios and its high temperature sensitivity especially relevant for determining severity degrees. These findings allow the authors to conclude that the proposed methodology, based on thermography and signal processing algorithms, has a high potential to become a diagnostic tool easily implementable in an industrial context.
DOI:10.1109/DEMPED.2017.8062416