FAULT DIAGNOSIS OF HIGH-SPEED ROLLING ELEMENT BEARINGS USING THE MACHINE LEARNING METHOD

Abstract

High-Speed Bearings fault diagnostics is a very critical aspect to increase the dependability and safety of modern industrial systems. Artificial Intelligence (AI) techniques have been gaining traction in academia and industry as an innovative and effective solution for defect detection and classification. As a result, this dissertation report aims to provide a comprehensive overview of the various processes involved in machine fault classification, as well as an introduction to various Machine Learning Algorithms such as k-Nearest Neighbour (k-NN), Naive Bayes, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Tree. The merits, drawbacks, and practical consequences of various AI algorithms are then examined. After that, there's also a comprehensive literature assessment of these AI algorithms in industrial applications. This dissertation work firstly focuses on classification of high speed bearing faults using various Machine Learning Techniques and comparing their performances. The Case Western Reverse University Bearing data is used for training and validation of different algorithms. Vibration response of the bearing are obtained and processed to obtain Time domain, Frequency Domain and Time-Frequency Domain features subsequently fed into machine learning models. Secondly, this dissertation focuses on using time domain features extraction on IMS bearing data which is a prognosis data and visualizing the data using PCA technique. The Time Frequency domain features extraction on CWRU bearing Data using Empirical Mode Decomposition (EMD) and then implement on Machine Learning models.