A modeling method of wide random forest multi-output soft sensor with attention mechanism for quality prediction of complex industrial processes

Complex industrial production processes often involve multiple product quality indicators that are interrelated. There exists a complex nonlinear mapping relationship between the operational input feature variables and multiple output target quality variables, making it difficult to accurately model through first-principle models. In order to fully capture the complex relationship between measurable variables and difficult-to-measure quality variables, and achieve accurate prediction of multiple output variables to meet the needs of practical industrial sites, this paper proposes a broad random forest-based multi-output soft sensor modeling method based on the idea of attention mechanism derived from the concept of broad learning systems. This method comprehensively considers the dynamic impact of different feature variables on the target quality indicators in actual production processes. The attention mechanism assists the soft sensor model in capturing contextual information better when dealing with long sequences, with a focus on the relevant parts related to the current task. Additionally, the interpretable random forest algorithm is employed as the weight estimator for the basic feature learning unit of Broad-based learning, enabling regression modeling of multiple target quality variables. The use of Broad-based random forest improves the model’s learning ability, interpretability, and generalization capability. To validate the reliability of the proposed method, it was applied to real industrial cases. The results demonstrated that the multi-output quality variable prediction performance of the proposed soft sensor outperforms existing soft sensors in terms of prediction accuracy. This indicates promising industrial application prospects for the proposed method. •A broad learning-based method for multi-output soft sensor modeling in industrial production processes.•The RF algorithm is employed to capture the weights of the basic feature learning units in the Broad Learning model.•The dynamic impact of different feature variables on the target quality indicator is taken into consideration.•The developed and tested soft measurement method is based on industrial process data.