Wavelet-based general methodology for multiple fault detection on induction motors at the startup vibration transient

Abstract

Fault preventive monitoring on induction motors has risen in order to reduce maintenance costs and increase their life expectancy. There are many developments for detecting a single induction motor fault using several methodologies and techniques. Different methodologies have been developed for multiple fault detection having the disadvantage of giving a qualitative result requiring an expert technician for estimating the motor condition, with the possibility of inducing observation errors. This work proposes a quantitative general methodology for online induction motor monitoring and identification of multiple faults in an automatic way, and its hardware processing unit for real time applications, based on the startup vibration transient analysis. The proposed methodology is tested on three different cases of study: a motor with broken rotor bars, an unbalanced motor shaft, and a motor with misaligned load. The results show that the proposed methodology is highly reliable for detecting different faults in induction motors with a certainty of 99.7%. The developed approach can be extended for detecting other faults by a proper calibration, thanks to its generalized nature.