Abstract

CABALLERO-PENA, Jairo Andres  and  ROSERO-GARCIA, Javier. Decentralized inter-turn fault diagnosis of induction motors based on wireless sensor networks. Dyna rev.fac.nac.minas [online]. 2021, vol.88, n.216, pp.237-246.  Epub May 24, 2021. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v88n216.88851.

Motor’s fault diagnosis has achieved multiples advances and has integrated different analysis and data classification techniques with the purpose of giving noise tolerance, electric transients tolerance and withstand changes of operating point; but these must be analyzed to achieve their integration in programmable devices and to identify their improvements. Therefore, a decentralized induction motor fault monitoring and diagnosis was developed and implemented, this was based on Wireless Sensor Networks - WSN and Support Vector Machine - SVM as data classifier. In this paper, Indicators were established based on Motor-Current Signature Analysis - MCSA, Fast Fourier Transform - FFT and Discrete Wavelet Transform DWT with which it is possible to validate and identify a differentiated behavior of incipient interturn fault of critical faults like phase-phase and phase-neutral short circuits when isolation deterioration is presented.

Keywords : decentralized analysis; fault diagnosis; induction motor; inter-turn fault; motor-current signature analysis; stator current; wireless sensor networks.

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