Fault Prediction of Ball Bearings using Machine Learning: A Review

Abstract

Machine learning and deep learning algorithms have shown positive outcomes in a variety of industries. The number of defects in machinery equipment is predicted to rise as the usage of smart machinery grows. The use of diverse algorithms to detect and diagnose machine faults is becoming more common. Using both open-source and closed-source data sets and machine learning methods, a variety of studies have been conducted and published. This paper reviews current work that uses the bearing data set to detect and diagnose equipment faults using machine learning and deep learning methods. In this paper, the working algorithm, result, and other relevant details are described, as well as the recently published studies.