Events7th International Electronic Conference on Sensors and Applications
Published
This submission belongs to the session D. Applications of the event 7th International Electronic Conference on Sensors and Applications
Published date
14 Nov, 2020
Citation
Guilherme Lucas, Marco Aurélio Rocha, Bruno Albuquerque de Castro, José Vital Leão, André Luiz Andreoli, Stator Winding Fault Phase Identification Using Piezoelectric Sensors in Three-Phase Induction Motors., in Proceedings of 7th International Electronic Conference on Sensors and Applications, 15 November–30 November 2020, MDPI: Basel, Switzerland, doi: 10.3390/ecsa-7-08183
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Stator Winding Fault Phase Identification Using Piezoelectric Sensors in Three-Phase Induction Motors.

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José Vital Leão 2
1. Department of Electrical Engineering, São Paulo State University (UNESP), Brazil
2. Department of Electrical Engineering, São Paulo State University (UNESP)
Abstract

Three Phase Induction Motors (TIMs) play a key role in industrial production lines. Due to its robustness and versatility, TIMs are commonly used to drive different devices like fans, conveyors, sieves, and compressors. However, these equipment are often exposed to mechanical and electrical faults. Among them, failures in stator winding insulation lead to severe damage to the TIMs and can cause operational interruptions. Therefore, several approaches have been developed to monitor electrical faults in induction motors. The acoustic emission (AE) stands out as an efficient non-destructive technique (NDT) for TIM diagnosis. In this work, the AE analysis was applied to detect winding insulation faults and identify which electrical phase was affected. To achieve this proposal, a TIM was subjected to insulation faults in each of the three electrical phases, and the acoustic signals were acquired by four piezoelectric sensors attached to the motor. These signals were processed using a new technique, which calculates the energy of a specific range of the signal spectrum and assigns the energy values of each piezoelectric sensor to a coordinate axis (x, y). By ploting the values for each fault condition, this technique allows the detection of insulation faults and correctly identifies the affected phase by clustering the resulting values. Finally, the proposed methodology presented satisfactory results in winding insulation diagnosing.

Keywords
Winding fault
Acoustic Emission
Piezoelectric Sensors
Induction Motors
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