Condition Based Monitoring of Ball Bearings Using Machine Learning

Abstract

Ball bearings are used in rotary machines such as turbines, generators, automobiles, and electric motors just to name a few. They operate in harsh conditions, heavy loads and shocks which deteriorate their health and if not monitored timely, can lead to costly damage. With the advancements in technology, various sensors are integrated to monitor machines’ health. An important health indicator of such machines is vibration signal data which provides meaningful insight into a variety of mechanical faults. Traditionally, bearing faults are analyzed in timefrequency domain which is incapable to classify the types of faults accurately. In this project, we used vibration signal data acquired by Korea Advanced Institute of Technology and ourselves and coupled it with machine learning algorithms to better understand the principles of these algorithms and how they perform. Additionally, data acquired by our setup is tested on these algorithms for validation.