Online SFRA characterization of a batch of induction motors for predictive maintenance

Giovanni Bucci, Fabrizio Ciancetta, Andrea Fioravanti, Edoardo Fiorucci, Simone Mari, Andrea Silvestri

Abstract

Asynchronous motors represent a large percentage of motors used in industry. Implementing predictive maintenance techniques can be justified in the case of engines that are of critical importance in the processes despite being of low cost. In these cases, the continuous monitoring of the motors requires noninvasive and online techniques, which allow the monitoring of motor characteristics over time to highlight potential trends that tend toward the condition of catastrophic failure. Online SFRA may be of interest in this scenario. In this article, this technique has been applied to a set of new asynchronous motors. They have been characterized under different load conditions. The results were used to determine transfer functions (TFs) with which it is possible to compare the TFs acquired by an engine to be monitored. The test system and the first experimental results are presented.

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IMEKO-TC4-2022-21.pdf

DOI

10.21014/tc4-2022.21

IMEKO TC

TC4 - Measurement of Electrical Quantities

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