An Adaptive Transition Process Recognition and Modeling Framework for Soft Sensor in Complex Engineering Systems

In industrial production processes, variations in operating conditions or task requirements often lead to multimodal characteristics in process data. Mode identification is therefore essential for effective process monitoring and soft sensing. However, conventional strategies usually overlook transient modes, which hinders the adaptability of soft sensing models during transitional phases. To address these challenges, this paper proposes an Adaptive Transition Process Recognition and Modeling Framework (ATPRMF). The framework consists of two key components: firstly, a Kullback-Leibler divergence-based transitional mode identification method that adaptively detects the onset and termination of transitional modes by analyzing distributional differences; and secondlya dynamic model fusion mechanism that integrates predictions from multiple models based on mode credibility, adapting to gradual distributional shifts and ensuring reliable predictions. Experimental validation on a real-world ball mill system and the benchmark Tennessee Eastman (TE) process demonstrate that the proposed framework significantly improves prediction accuracy and robustness compared to conventional approaches.