A DTW-Gaussian Spatiotemporal Self-Attention Network and Its Application in Industrial Fault Diagnosis With Unequal-Length Sensor Data

Equipment operating in intermittent industrial processes generates complex system coupling and unequal-length sensor data, which are considered significant challenges for fault diagnosis in industrial production. To address these challenges, a generic fault diagnosis approach, the dynamic time warping Gaussian spatiotemporal self-attention network (DGSSANet), is proposed. dynamic time warping combined with Gaussian blur is employed to handle sensor signal complexity, transforming unequal-length data into warping matrices of equal dimensions. The inclusion of spatiotemporal self-attention, with the multiscale temporal attention module and multielement spatial self-attention module, enhances the ability of DGSSANet to capture spatial and temporal relationships. DGSSANet is validated through benchmark experiments on bearing datasets and applied to excavator cases, with its interpretability analyzed via visualization. The results demonstrate that DGSSANet addresses the challenges of fault diagnosis in intermittent production processes effectively.