Deep Learning Techniques for Early Fault Detection in Bearings: An Intelligent Approach

Bearings are essential for spinning machines. An unexpected bearing failure could disrupt production. This study describes a sophisticated method for diagnosing deep groove ball bearing issues. We designed and built an experimental setup to collect precise data in many scenarios, including inner race fault, outer race fault, cage fault, and normal state. Machine learning (ML) and deep learning (DL) algorithms have improved image processing, speech recognition, defect detection, item identification, and medical sciences. Experts anticipate a surge in equipment problems as intelligent machinery becomes more prevalent. Deep learning methods for equipment failure detection and diagnosis have increased steadily. Research papers have used deep learning to study and share open-source and closed source data. The Case Western Reserve University (CWRU) bearing data set identifies abnormalities in machinery bearings. Popularity makes this dataset simple to access. This dataset is 'ideal' for model verification and is widely accepted. This article describes current deep learning research using the CWRU bearing dataset to diagnose machinery faults precisely. Using the CWRU dataset, this article has the potential to be of significant service to future academics who desire to begin their work on the detection and diagnosis of machinery failures. This is our view.This paper focuses on utilizing the CWRU bearing dataset combined with Elastic Weight Consolidation (EWC (algorithm to achieve a notable accuracy of 97.06%. The streamlined approach emphasizes the use of raw data and advanced methodologies, showcasing the significance of achieving high diagnostic accuracy while providing a reliable alternative to conventional fault classification techniques.