Fault Diagnosis of Harmonic Reducers Based On a FDG-Resnet Joint Methodology

Harmonic reducers(HRs) are widely used in various industrial fields due to its advantages of high precision, high transmission efficiency and compact structure. However, the vibration signal of the HRs are complex, making it difficult to diagnose faults accurately. In this paper, a method based on frequency domain Gramian angular field (FDG) and Deep residual network was proposed to classify the fault states of the HRs. First, the vibration signals were converted into twodimensional images using Gramian angular field (GAF) coding, and a two-dimensional discrete Fourier transform (2D-DFT) were performed to transform the images from the spatial domain to the frequency domain; The training accuracy of traditional models is not very high due to their low number of network layers. The deep residual network not only has a very deep network layer, but also can guarantee the accuracy of the model under certain conditions, so the deep residual network is introduced to train the model. Finally, experiments were designed using data from a real industrial HRs health monitoring platform to validate the proposed method. Under fixed operating conditions, the proposed method achieved an accuracy of 99%, which was an improvement of 0.3% compared to ResNet50, 3.5% compared to VGGNet-16, and 2.9% compared to the Swin-Tiny model. Under variable load conditions, it achieved an accuracy of 99.4%, an increase of 0.7% compared to ResNet50, 5.5% compared to VGGNet-16, and 5.6% compared to Swin-Tiny. Under variable speed conditions, it achieved an accuracy of 94.3%, an improvement of 0.9% compared to ResNet50, 0.1% compared to VGGNet-16, and 9.3% compared to Swin-Tiny.