A Deep Learning Framework for the Classification of Bearing Faults

In this Paper, the faults in the bearings are detected and classified using predictive maintenance technique. Because, it is gaining significant importance in recent years due to its capability to monitor machines from shutting down, reduce downtime, and optimize maintenance schedules. Predictive maintenance plays a crucial role in preventing unexpected failures and optimizing maintenance activities in industrial systems. This paper tells about application of deep learning networks for the classification of bearing faults. Deep learning techniques have demonstrated exceptional performance in various domains, and their potential in predictive maintenance for bearings is gaining considerable attention. The proposed model classifies the Faults in Bearings using CNN which achieves an accuracy of 83.3%. This paper also provides an overview of the use of deep learning networks in bearing fault classification. This paper includes an Introduction, Literature Review, Bearing Fault Classification, Experimental Results, and Conclusion.