Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors

Maciej Skowron,Teresa Orlowska-Kowalska,Marcin Wolkiewicz,Czeslaw T Kowalski

doi:10.3390/en12122392

Abstract

This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed.

Highlights

IntroductionThe most frequently occurring induction motor (IM) defects are bearing damages, which belong to the group of mechanical failures, constituting approximately 40% of all motor faults
During the operation of induction motor (IM) drive systems, various types of mechanical and electrical damages can occur, which should be detected at the earliest possible stage in order to avoid emergency shutdowns of the drive, resulting in downtime of industrial equipment and associated financial losses.The most frequently occurring IM defects are bearing damages, which belong to the group of mechanical failures, constituting approximately 40% of all motor faults
The conducted tests have demonstrated the effectiveness of the use of different neural network structures in the detection process of induction motor faults

Summary

Introduction

The most frequently occurring IM defects are bearing damages, which belong to the group of mechanical failures, constituting approximately 40% of all motor faults. Electrical damages, mainly related to the motor windings, are a serious quality and quantity problem. These failures are usually associated with short-circuits in the stator windings (about 38% of all IM faults) and damages to the rotor bars and rings in the squirrel-cage rotors (about 10%) [1]. The detection of damage to the windings of electric motors has been analyzed in many articles, and a review of the methods used can be found among others in [2]. The damage detection methods currently used in the technique can be divided into: methods using diagnostic signal analysis, e.g., [3,4,5], statistical methods based on signal properties, e.g., [6,7,8,9]

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