Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions

Bearing faults in rotating machinery can lead to significant economic losses due to downtime and pose serious safety risks. Accurate fault diagnosis is crucial for effective condition monitoring. Traditional methods for diagnosing bearing faults under noisy conditions often rely on complex data preprocessing and struggle to maintain accuracy in high-noise environments. To address this challenge, this paper proposes an end-to-end Discrete Wavelet Integrated Convolutional Residual Neural Network (DWCResNet) for bearing fault diagnosis. The model incorporates Discrete Wavelet Transform (DWT) layers to replace traditional downsampling operations in convolutional neural networks, decomposing input signals into low-frequency and high-frequency components to effectively remove high-frequency noise and extract fault features, thereby improving diagnostic performance. The cyclic learning rate strategy enhances training efficiency. Experiments conducted on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets demonstrate that DWCResNet achieves higher diagnostic accuracy and noise robustness under various conditions, providing an efficient solution for bearing fault diagnosis in complex noisy environments.