A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park's Vector Components of Current and Voltage.

Natalia Koteleva,Yuriy Zhukovskiy,Georgii Baranov,Nikolay Korolev

doi:10.3390/s21237900

Abstract

This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN-classifier). Experiments are carried out in laboratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN-classifier. A method of providing the data into the classifier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data.

Highlights

Today’s trend in industrial companies is to improve automation systems in all areas of operation [1,2]
The same trend can be seen in monitoring and maintenance systems for electrical machines
The main hypothesis of the paper is that a soft sensor, which is a mathematical apparatus combining the Park vector transformation and a classifier based on an artificial neural network (ANN-classifier), will enable real-time detection of bearing defects in electromechanical machines

Summary

Introduction

Today’s trend in industrial companies is to improve automation systems in all areas of operation [1,2]. This is primarily due to the digitalization of production [3]. The digital transformation of the energy sector is rapidly bringing new solutions to the market [6,7] This development goes hand in hand with an uncontrolled increase in computing and instrumentation capacity. The same trend can be seen in monitoring and maintenance systems for electrical machines This in turn prevents the development of evaluation maps drive conditions for different process equipment topologies [8,9]

Objectives

Findings

Methods

Conclusion