Fault diagnosis of rolling bearing under time-varying speed conditions based on EfficientNetv2

Abstract

The rolling bearing is a widely-used component in engineering. The fault diagnosis of rolling bearings is key to ensuring the normal operation of equipment. At present, research into the fault diagnosis of rolling bearings mainly focuses on the analysis of vibration data under constant working conditions. However, when dealing with practical engineering problems, equipment frequently operates at variable speed. To analyse the vibration data in the case of frequency conversion and accurately extract the fault characteristic frequency is a challenge, especially when the fault characteristics are weak. In addition, traditional vibration characteristic analysis requires professional technicians to supervise the operation of the equipment, which requires a certain professional ability of the staff. Based on the above two problems, this paper proposes a rolling bearing fault diagnosis model under time-varying speed working conditions, based on the EfficientNetv2 network. This method uses a short-time Fourier transform to convert a one-dimensional vibration signal into a two-dimensional image signal, and uses the advantages of an image recognition network to realize the fault diagnosis under time-varying speed conditions. After training the network, based on transfer learning, the experimental data verify that the accuracy of the results reaches 99.9 ± 0.1%, even in the case of weak fault characteristics, and there is no need for professional technicians to supervise and diagnose once the model is trained, which is conducive to practical application.