A two‐layer temporal stochastic configuration broad learning system for complex industrial soft sensing

Abstract Stochastic configuration broad learning system (SCBLS) has demonstrated notable advantages in industrial soft sensing due to its computational efficiency and supervised node configuration. However, conventional SCBLS overlooks the dynamic characteristics of industrial data, which limits the prediction accuracy of soft sensor models. To address this limitation, this study proposes a two‐layer temporal SCBLS (TT‐SCBLS) for industrial soft sensor modelling. The proposed framework consists of two key modules: an output‐related dual‐layer temporal feature extraction module and an enhancement node stochastic configuration module. The former is used to build the primary mapped features, where the first layer employs quality‐related slow feature analysis (QSFA) to extract intrinsic slow features, mitigating the impact of data noise, and the second layer utilizes a cycle reservoir with regular jumps (CRJ) network to capture temporal dependencies in the process data. The later introduces the stochastic configuration algorithm for incremental node expansion at the enhancement layer, ensuring efficient model adaptation. Additionally, a kernel Shapley additive explanation (SHAP) model is integrated to quantify the contributions of input variables, enhancing model transparency. The proposed method is validated on two benchmark industrial systems, with experimental results confirming its superior prediction performance compared to existing approaches.