Identifying Faults of Rolling Element Based on Persistence Spectrum and Convolutional Neural Network With ResNet Structure

The task of accurately bearing fault diagnosis of the rotary machinery from the measured signal remains a major problem that attracts a lot of attention. This paper proposed a new approach to build an efficient bearing fault diagnostic model for rotary machinery. The model is based on the persistence spectrum image and convolutional neural network (CNN) with ResNet structure. The persistence spectrum is extracted from the envelope of the raw vibration signal. Then, the persistence spectrum image is constructed based on short-time Fourier transform, which presents a new relationship between the frequency, magnitude, and energy of each signal with time, which the traditional spectrum analysis methods have not been given before. To explore the discriminant features from the persistence spectrum image of the envelope signal, an improved CNN with ResNet structure allows direct connection feature maps from the lower-level layer to the higher-level layer. That helps to exploit the granularity features in the low-level layer which can be lost when feedforward through adjacent layers in a traditional CNN. As a result, the proposed model operates efficiently with high accuracy not only under various working loads but also under noise conditions. Besides, its performance is very satisfactory compared to other types of two-dimensional images and other state-of-the-art diagnosis models. Overall, the proposed approach is highly feasible for an intelligent bearing fault diagnostic model.