Research progress on bearing fault diagnosis with signal processing methods for rolling element bearings

Abstract

As a prerequisite for rotating machinery to operate effectively, rolling element bearings play an essential role. The focus of condition monitoring has initially been on defect identification, then on its measurement, and eventually on automatic defect prediction. The improvement in signal processing has made this breakthrough possible. The quality of characteristics taken from the bearing signals strongly impacts how effective these techniques are. Aiming to provide the researchers with the option to choose and implement the optimum signal analysis method, the authors have described numerous signal processing techniques used to diagnose faults in rolling element bearings. The research study examines several important studies and explains their relevance to locating rolling bearing defects. It analyzed recent research, ones from the past, and developments in the field of diagnosing bearing defects. The main goal of the research is to investigate different vibration signal processing and analysis methods for locating and evaluating bearing faults. After that, each of these subjects is rigorously analyzed in order to draw conclusions, spot new trends, and pinpoint areas that still need more research. This article is meant to serve as a guide for those who operate in the condition monitoring domain.