Mechanical fault detection in electric motors measured by a digital signal processing device in an optical mouse

Abstract

The objective of this work is to study the application of a new methodology for diagnosing mechanical faults in electric motors through vibration analysis and using a non-contact approach based on a digital signal processing (DSP) device in an optical computer mouse. The mechanical fault used to test the proposed methodology was related to bearing damage. An experimental bench was set up to test the detection of damage in bearings. The analyses of the measured signals were performed in both time and frequency domains. Pattern recognition methods were used to classify the bearing state as damaged or not damaged. The electric motor was tested with two sets of bearings: a new set and a damaged set. The results demonstrated that the proposal for the prediction and diagnosis of faults was an efficient and promising technique because it is non-destructive and non-invasive due to the absence of contact between the sensor and the motor. Based on the concept of predictive maintenance, the proposed method has the potential to become an efficient and low-cost tool for predicting failures.