Deep Learning-Based Bearing Fault Detection Using 2-D Illustration of Time Sequence

Dhiraj Neupane, Jongwon Seok

Abstract: With the rapid development of science and technology, the production facilities are also growing advanced. An intelligent production facility is the outcome of smart systems used within a factory. The smart factories yield more production; thus, the faults in the machinery are prompt to increase when they are operated on a daily basis and for almost all applications. Different deep learning-based methods have been used and implemented in detecting and diagnosing bearing faults using raw vibration data. To detect and analyze the machinery bearing faults, we have proposed a deep learning-based convolutional neural network, which uses the 2-D image representation of 1-D raw vibration data from the Case Western Reserve University (CWRU) bearing dataset as input. With the use of the data augmentation technique for increasing training data, the proposed model has achieved 99.38% accuracy. The proposed method is computationally less expensive and simple than most of the complex algorithms used for detecting and diagnosing the bearing faults.