Natural Modal Sketching Network: An Interpretable Approach for Bearing Impulsive Feature Extraction
提出自然模态素描网络（NMSNet），将故障机理融入网络设计，实现强噪声下轴承冲击特征的可解释、鲁棒提取，并通过仿真和实验验证了其有效性和噪声鲁棒性。
Impulsive feature (IF) response is an essential indicator for rolling bearing fault. However, it is overwhelmed by strong noise and difficult to extract in real scenes. Although deep learning-based methods are powerful in feature extraction, their logic and extracting principles possess weak interpretability and credibility. Their further implementation is hampered. In this article, a natural modal sketching network (NMSNet) is constructed to achieve robust and credible bearing IF extraction. First, the modal response is designed as a convolutional kernel of NMSNet, and the forward propagation logic is interpreted as natural modal sketching, including modal response recovery and weighted superposition. The logic derives from the fault mechanism and brings solid credibility to NMSNet. Second, a novel correction algorithm is developed to interpret the extraction principle of NMSNet in theory and achieve noise elimination due to its filter nature. Third, NMSNet realizes adaptive modal sketching via the formulated weighted fusion strategy and training constraint. Finally, simulation and experiment have been carried out to verify the effectiveness and noise robustness of NMSNet. The fault-related interpretability analysis confirms the knowledge acquisition of NMSNet, which strengthens the credibility of IF extraction.