Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Abhisar Chouhan,Purushottam Gangsar,Rajkumar Porwal,Christopher K Mechefske

doi:10.21595/vp.2020.21334

Abstract

This paper describes an Artificial Neural Network (ANN) based fault diagnosis methodology for Induction Motors (IM) operating under the same conditions for various speeds and loads. In this study, ten different IM fault conditions are considered. We considered five mechanical faults (bearing fault, unbalanced rotor, misaligned rotor, bowed rotor, rotor with broken bar), four electrical faults (phase unbalance fault with two levels of severity, stator winding fault with two levels of severity), and one healthy motor condition. The current and vibration signals were considered in this work as these signals are generally considered to be the most efficient for the detection of mechanical and electrical faults in IM when used simultaneously. A machine fault simulator was used for the generation of vibration and current signals from different fault conditions. An ANN model was developed in which raw time domain vibration (in three directions) as well as current (in three phases) data are used simultaneously as input and then the fault diagnosis (training and testing) is performed. In this work, the fault diagnosis was attempted when testing was done for the same operating conditions as training. The developed fault diagnosis methods were found to be robust for various operating conditions (speeds and loads) of the IM.

Highlights

Induction motors (IMs) are most often utilized in different types of commercial applications such as machine tools, petrochemicals facilities, textile mills, power plants, and farming
The multi-class induction motor fault diagnosis is performed after obtaining the necessary data sets from raw time domain data with the help of the Artificial Neural Network (ANN) classifier
ANN-based fault diagnosis is developed for IM for various operating conditions

Summary

Introduction

Induction motors (IMs) are most often utilized in different types of commercial applications such as machine tools, petrochemicals facilities, textile mills, power plants, and farming. In the continuation of the AI based research work on IM fault diagnosis, Gangsar and Tiwari [8,9,10] have successfully attempted the diagnosis based on Support Vector Machine They have considered time, frequency as well as wavelet analysis for the diagnosis of mechanical and electrical faults of IM. Jigyasu et al, [13] presented a comparison of current and vibration based diagnosis using artificial neural networks for different types of motor failures such as bearings, stators, rotors and eccentricity. Li and Mechefske [15] presented the comparison of induction motor fault detection using stator current, vibration, and acoustic methods. The ANN based IM fault diagnosis is developed for diagnosing mechanical and electrical faults as well as their severity simultaneously for similar operating conditions. The robustness of the developed methodology has been checked for various operating conditions (i.e. speed and load) of the IM which is one of the critical things in any AI based fault diagnosis

Introduction to artificial neural network

Experimental setup and data processing

ANN based fault diagnosis

Findings

Conclusions