Fault Diagnosis of Rolling Bearings Based on CWT- RT Wavelet Scattering Networks

Abstract

Aiming at the non-stationary, nonlinear and noise-sensitive characteristics of rolling bearing vibration signals, as well as the low recognition accuracy of traditional deep learning in small-sample scenarios, this paper proposes a rolling bearing fault diagnosis method combining Continuous Wavelet Transform with Ridge Tracking (CWT-RT) and Multi-Scale Wavelet Scattering Network. First, Variational Mode Decomposition integrated with Cramer Von Misses statistic (VMD-CVM) is adopted to denoise the original signal and improve the signal-to-noise ratio. Then, CWT-RT is used to transform the denoised signal into time-frequency spectrograms for intuitive time-frequency feature representation. Multi-Scale Wavelet Scattering Network is further applied to extract multi-level structural features, which are fed into Multi-Layer Perceptron (MLP) to realize bearing fault identification. To eliminate data leakage, all original raw vibration files are split into training and test sets at a 7:3 ratio before sliding window sampling. Validation experiments on bearing datasets from South Ural State University, CWRU, and XJTU-SY show that the diagnostic accuracies on two small-sample conditions reach 98.78% and 98.09%, respectively. the 95% confidence intervals for the two accuracy values are [98.21%, 99.15%] and [97.43%, 98.67%], respectively. Across 10 repeated experiments, p-values < 0.001 confirm the statistical significance of the results. The model maintains high accuracy under different loads and noise levels (0/5/10/15 dB). Comparative and ablation experiments verify that the method has high diagnostic accuracy, strong noise robustness and superior small-sample learning ability, with each module effective, and the full-pipeline latency meets the real-time requirements of IoT edge deployment, providing support for industrial engineering applications.