Adaptive Multiresampling Learning Based on Dual-Scale Feature Aggregation for Industrial Quality Prediction

Quality prediction is essential for optimizing operations and making timely decisions in industrial processes. However, the dynamic nature of industrial data, characterized by different sampling periods, presents significant challenges for quality prediction. The relationship between quality indices and various industrial data with differing sampling periods is complex and dynamically correlated in both spatial and temporal dimensions. To address this issue, we propose an adaptive multiresampling learning (AMRL) network that performs deep spatial-temporal feature mining and extraction from dual-scale data, facilitating multistep industrial quality prediction. The AMRL network leverages principal component scores of fast-scale process data and an adaptive multiresampling module to construct multiple fast-scale resampling channels adaptively. The cross-scale deep convolutional neural network and the multichannel self-attention module are then employed to capture spatial-temporal features and selectively focus on critical regions within the multiresampling sequences. We compared and evaluated the proposed method against eight state-of-the-art methods using real industrial datasets. The comparison results demonstrate the superior performance of the AMRL in multistep industrial quality prediction.