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

[Display omitted] •DSP device in optical mouse can be used as a sensor of vibration in electrical motors.•Damaged bearings in electrical motors could be identified using the proposed method.•Mechanical vibration of electrical motors could be separated when using damaged and non-damaged bearings. The...

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Veröffentlicht in:Measurement : journal of the International Measurement Confederation 2019-05, Vol.138, p.350-355
Hauptverfasser: Ribeiro, Diogo Aranha, Ferreira, Danton Diego, Pereira, Daniel Augusto, Braga Junior, Roberto Alves, Nunes, Rodrigo Dantas
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Sprache:eng
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Zusammenfassung:[Display omitted] •DSP device in optical mouse can be used as a sensor of vibration in electrical motors.•Damaged bearings in electrical motors could be identified using the proposed method.•Mechanical vibration of electrical motors could be separated when using damaged and non-damaged bearings. 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.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2019.02.050