Assessment of failure to the stator winding of the induction motor by means of deep neural network

Sebastian Kuroczycki,Konrad Górny,Wojciech Pietrowski,R Nawrowski,J Lorenc,A Tomczewski,Z Nadolny,K Budnik,A Bugała,J Jajczyk,L Kasprzyk

doi:10.1051/itmconf/20192801049

Sebastian Kuroczycki, Konrad Górny + Show 9 more

Open Access

https://doi.org/10.1051/itmconf/20192801049

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Abstract

Due to the fact that inter-turn short-circuits are the ones of the most common causes of damage to stator of induction motors, research on their early detection is still gaining in importance. The scientific novelty in the presented article is an approach in which a decision element informing about the failure of stator of induction machine is a deep artificial neural network. In the learning process, torque waveforms subjected to a continuous wavelet transform were used. In order to classify of the stator winding failures the accelerator of artificial neural networks was used.

Highlights

Studies carried out in recent years show that induction motors are the most commonly used motors in industry
Many studies devoted to the diagnostics of electrical machines show that the most common damage of stator of the induction motors are related to the inter-turn short-circuits [4]
The article presents a new method of diagnostics of electrical machines using deep neural networks

Summary

Introduction

Studies carried out in recent years show that induction motors are the most commonly used motors in industry. Many studies devoted to the diagnostics of electrical machines show that the most common damage of stator of the induction motors are related to the inter-turn short-circuits [4]. Due to the fact that the effects of inter-turn faults are visible in the waveforms of such values as current or torque of the motor, these signals can be used to failures identify.

Results

Conclusion