FEMSN: Frequency-Enhanced Multiscale Network for fault diagnosis of rotating machinery under strong noise environments
This addresses fault diagnosis for industrial rotating machinery, but it appears incremental as it builds on existing CNN methods with specific enhancements for noise environments.
The paper tackles fault diagnosis in rotating machinery under strong noise by proposing FEMSN, a CNN-based model with a Fourier Adaptive Denoising Encoder Layer and Multiscale Time-Frequency Fusion module, achieving improved robustness and representation capability as validated in case studies.
Rolling bearings are critical components of rotating machinery, and their proper functioning is essential for industrial production. Most existing condition monitoring methods focus on extracting discriminative features from time-domain signals to assess bearing health status. However, under complex operating conditions, periodic impulsive characteristics related to fault information are often obscured by noise interference. Consequently, existing approaches struggle to learn distinctive fault-related features in such scenarios. To address this issue, this paper proposes a novel CNN-based model named FEMSN. Specifically, a Fourier Adaptive Denoising Encoder Layer (FADEL) is introduced as an input denoising layer to enhance key features while filtering out irrelevant information. Subsequently, a Multiscale Time-Frequency Fusion (MSTFF) module is employed to extract fused time-frequency features, further improving the model robustness and nonlinear representation capability. Additionally, a distillation layer is incorporated to expand the receptive field. Based on these advancements, a novel deep lightweight CNN model, termed the Frequency-Enhanced Multiscale Network (FEMSN), is developed. The effectiveness of FEMSN and FADEL in machine health monitoring and stability assessment is validated through two case studies.