Bearing-Fault-Detection-using-Deep-Learning-approach

Detection and multi-class classification of Bearing faults using Image classification from Case Western Reserve University data of bearing vibrations recorded at different frequencies. Developed an algorithm to convert vibrational data into Symmetrized Dot Pattern images based on a Research paper. Created an Image dataset of 50 different parameters and 4 different fault classes, to select optimum parameters for efficient classification. Trained and tested 50 different datasets on different Image-net models to obtain maximum accuracy. Obtained an accuracy of 98% for Binary classification of Inner and Outer race faults on Efficient Net B7 model on just 5 epochs.