Bearing Fault Detection of a Single-phase Induction Motor Using Acoustic and Vibration Analysis Through Hilbert-Huang Transform

Abstract

Bearings are widely used as a low friction component for rotating machines. Engaging research on a bearing is vital to increase life span and improve the reliability of a motor. The main objective of this study is to design a bearing fault detection system for a single-phase induction motor using acoustic and vibration analysis through Hilbert-Huang Transform (HHT). An experimental se-tup was developed to measure the vibration and acoustic signal of a motor rated at 230V and with 125W nominal power. This study introduced an advanced approach to optimizing signals using MATLAB software based on the Hilbert-Huang Transform (HHT) technique. HHT can be used to describe nonlinear distorted waves in detail. Empirical Mode Decomposition (EMD) is the one that deals with the nonlinear and non-steady-state processes to extract complex signals into a finite number of Intrinsic Mode Functions (IMF) which should be achieved for Hilbert Transform (HT) to illustrate the energy time-frequency response of a system. This study successfully developed a single-phase induction motor fault detection system using HHT. The results showed that the inner race fault could be detected with 69% accuracy, outer race fault has 75%, Ball bearing fault has 87%, and contaminated bearing fault lubrication has 68%. The overall accuracy of the detection system could be achieved up to 74.75% accuracy.)