Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types

Abstract

Purpose

Electric motors are present in most industries today, being the main source of power. Thus, detection of faults is very important to rise reliability, reduce the production cost, improving uptime and safety. Vibration analysis for condition-based maintenance is a mature technique in view of these objectives.

Design/methodology/approach

This paper shows a methodology to analyze the vibration signal of electric rotating motors and diagnosis the health of the motor using time and frequency domain responses. The analysis lies in the fact that all rotating motor has a stable vibration pattern on health conditions. If the motor becomes faulty, the vibration pattern gets changed.

Findings

Results showed that through the vibration analysis using the frequency domain response it is possible to detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar and bearing fault condition.

Originality/value

The proposed methodology is verified through a real experimental setup.

Citation

Ribeiro Junior, R.F., Areias, I.A.d.S. and Gomes, G.F. (2021), "Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types", Sensor Review, Vol. 41 No. 3, pp. 311-319. https://doi.org/10.1108/SR-02-2021-0052