Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Rafia Nishat Toma,Jong-Myon Kim

doi:10.3390/app10155251

Abstract

Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.

Highlights

Among rotating machinery, induction motors (IM) are broadly used in manufacturing industries such as transportation, petrochemicals, and power systems due to low cost, high reliability, robust design, and higher efficiency under full load
Electrical current analysis has emerged as an intelligent solution that simplifies the fault diagnosis process with a small number of sensors
Ensemble machine learning methods was proposed for IM bearing fault diagnosis

Summary

Introduction

Induction motors (IM) are broadly used in manufacturing industries such as transportation, petrochemicals, and power systems due to low cost, high reliability, robust design, and higher efficiency under full load. IMs need to remain operative for a long duration and commonly under harsh operating environments accompanied with regular wear, which cause mechanical and electrical stresses. These can lead to unexpected failure in gears and bearings, which are the significant machine components of IM. The machine learning (ML)-based fault analysis approaches are turned out as a powerful and prevalent approaches in the area of continuous health monitoring of rotating machineries, as they have the ability to extract valuable information from the considerable amount of historical data [1,2,3]

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