Vibration Signal Processing Based Bearing Defect Diagnosis with Transfer Learning

It is very important to diagnose the fault condition of the bearing machines in order to make the machine run healthier. There are many successful studies in the detection of failures in the bearing machine made by conventional machine learning methods. However, these studies produce successful results in cases where the machines operate under the same condition and feature space is the same. Therefore, a deep learning-based diagnostic has been proposed for changing machine operating conditions. In the scope of our study, Keras and Tensorflow libraries, CNN network from scratch, VGG16 model and VGG19 deep learning models have been used for the classification of vibration images. Freeze the weights for transfer learning and remove the last fully connected layer to update the network in a problem-specific manner. The number of iterations and batch size has been determined by experimental studies. In this study, four faulty conditions have been successfully classified. While the accuracy rate of CNN network from scratch is 25%, the accuracy rate obtained by the VGG16 transfer learning method is 93% and the loss rate is 0.17% and the accuracy rate obtained by the VGG19 transfer learning method is 95% and the loss rate is 0.13%.