Bearing fault diagnosis using signal processing and machine learning techniques: A review

Abstract

Abstract In the majority of machines, bearings are among the most crucial components. Bearings are so important that they have been the subject of intensive research and ongoing development throughout the years. Often, bearing fails to reach its expected service life, resulting in failures that cause economic losses. Therefore, there has been a growing interest in research on bearing failure diagnosis systems due to the availability of condition monitoring techniques. Fault feature extraction techniques with the application of signal processing methods and machine learning techniques introduce an Intelligent Fault Diagnosis system that can identify and diagnose the bearing faults. Many researchers have been interested in such techniques in recent decades, which use artificial intelligence to diagnose machine health conditions. In this article, the authors have explored certain fault diagnosis methodologies based on signal processing and machine learning. From the discussed literature review, a research gap for future work has been defined.