INTELLIGENT BEARING FAULT DIAGNOSTICS WITH MACHINE LEARNING AND DEEP LEARNING FRAMEWORKS: A COMPREHENSIVE AND COMPARATIVE ANALYSIS

Abstract

In this research work, we systematically investigate the deep learning and classical machine learning approaches for bearing fault diagnostics. Bearings are the most critical mechanical components as their operating conditions have a direct effect on the safety and working of the mechanical equipment. As per the demand of industry users, researchers have always paid great attention to the quality, durability, and service life of bearings. Recent developments in machine learning and especially in deep learning have framed new research areas that have developed an increased interest in both industry experts and academic researchers. In this research, we first investigate the working, characteristics, and limitations of available machine learning and deep learning methods in bearing fault diagnostics applications such as artificial neural networks (ANNs), deep belief networks (DBNs), support vector machine method, etc. Apart from current methods in available literature, the new methods and functionalities are also analyzed and a detailed section on potential methods along with data sets is also dedicated so that it helps other researchers to extend their research. In last, a detailed comprehensive and comparative analysis between the machine learning and deep learning methods is also provided along with discussion section which is intended to facilitate in applying these algorithms for specific applications. The future research section is also added to discuss the current research limitations and potential research areas.