No abstract

Rolling bearing is a widely used component in engineering. Fault diagnosis of rolling bearing is the key issue to ensure the normal operation of equipment. At present, the research on fault diagnosis of rolling bearing mainly focuses on the analysis of vibration data under constant working conditions. Nevertheless, when dealing with practical engineering problems, the operation of equipment is frequent in the case of variable speed. To analyze 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, the traditional vibration characteristic analysis requires professional technicians to supervise the operation of the equipment, which requires certain professional ability of the staff. Based on the above two problems, this paper proposes a rolling bearing fault diagnosis model under time-varying working conditions based on EfficientNetv2 network. This method uses short-time Fourier transform to convert one-dimensional vibration signal into two-dimensional image signal, and uses the advantages of 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 reach 99.9 ± 0.1% even though in the case of weak fault characteristics, and there is no need for professional technicians to supervise and diagnose after model training, which is conducive to practical application.

No abstract

To solve the problems of low precision of weak feature extraction, heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage (UHV) equipment key parts, an automatic feature extraction algorithm is proposed. Firstly, the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method. Then, binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation. Finally, the automatic identification of weld defect area is realized based on the sequential traversal of binary tree. Several characteristic analysis dimensions are established for weld defects of UHV key parts, including defect area, perimeter, slenderness ratio, duty cycle, etc. The experiment using the weld detection image of the actual production site shows that the proposed method can effectively extract the weak feature information of weld defects and further provide reference for decision-making.