Multi-Fault Bearing Classification Using Sensors and ConvNet-Based Transfer Learning Approach

Abstract

The development of sensor technology and modern computing allows the fault diagnosis of rotating machinery to exploit under different working conditions. As an effect, digital health monitoring of machine is performed based on the actual data values obtained from the multi-sensor, and also, it supports to design the data-rich datasets using multi-sensor. Thus, it motivates to develop the data-driven approaches for fault diagnosis based on multi-sensor data. However, they require a large amount of historical evidence for the development, and in a real-world situation, usually sufficient data is not available for building the data-driven model. As a result, they become less competent to perform the task of fault diagnosis. Therefore, in this paper, the transfer of knowledge from data-rich datasets is proposed to identifying the fault in new working conditions of the related domain. To transfer the experience, firstly the data has transformed to kurtogram, which is the fusion of multilevel kurtosis values and then convolutional neural network-based transfer learning approach has presented for fault diagnosis under various operating conditions. Also, the simultaneous change in speed and load in source and target domain has analyzed to explore the effect of the proposed method on performance of fault diagnosis. Further, the issue of negative transfer of knowledge has investigated by determining the association of speed as well as a load between the source and target domain. The proposed method is tested using two vibration datasets, and results demonstrate the decent performance of fault diagnosis using transfer learning approach.