Performance Evaluation of the Signal Processing Based Transfer Learning Algorithm for the Fault Classification at Different Datasets

The detection of rolling bearing faults of rotating machines is very important in reducing production losses, financial losses, and accidents in the manufacturing industry. Therefore, various methods have been developed so far for the detection of rolling bearing faults. Recently, transfer learning-based methods are getting popularity in the area of artificial intelligence for this purpose. In this study, the performance of a transfer learning algorithm for fault classification using scalogram images has been studied at different load conditions of the collected dataset, size of the dataset, and training-testing ratio of the dataset for the benefit of future researchers.