A Spatiotemporal Dynamic Wavelet Network for Infrared Thermography-Based Machine Prognostics
提出一种时空动态卷积小波网络，通过自适应调制卷积核、可学习提升小波结构和双线性特征融合，从红外热图像中提取退化特征，实现机器健康状态预测。
Infrared thermography is increasingly exploited to track mechanical degradation in a noncontact manner, readily available for further prognostics. Recently, wavelet networks have coalesced deep learning and wavelet transform (WT), expected to achieve data-driven and interpretable prognostics. However, traditional wavelet networks neither possess enough adaptability to extract degradation-related features nor sufficiently fuse learned wavelet coefficients. Thus, this article presents a spatiotemporal dynamic convolution-based wavelet network to handle the above difficulties in industry. First, a spatiotemporal dynamic convolution layer is presented to flexibly modulate kernels according to input samples and the multidimensional kernel space. Second, a learnable lifting scheme structure is constructed to perform signal-adapted WT while incorporating crucial properties to link the optimization of lifting filters and degradation-related feature learning. Finally, a bilinear feature fusion is implemented to jointly represent extracted wavelet energy across decomposition levels, facilitating synergistic optimization. The superiority of the proposed method is illustrated through infrared degradation image datasets.