Multi-model strategy based evidential soft sensor model for predicting evaluation of variables with uncertainty

Abstract

The traditional soft sensor methods cannot be applied in the cases where uncertainty exists. To predict evaluation of variable with uncertainty, a novel method, called multi-evidential soft sensor model, was proposed based on evidence theory and multi-model strategy. In such method, the overall multi-model on the whole data space is established by weight sum of the multiple local models on local data spaces. For arbitrary given input vector x, each local model provides a prediction regarding the value of the output variable y, in the form of fuzzy belief assignment (FBA), defined as a collection of fuzzy sets of values with associated masses of belief. The output FBA is computed using a parametric, instance-based approach: imprecise training samples in the neighbourhood of x are considered as sources of partial information on the response variable y, and provide pieces of evidence reflecting the values taken by the response y; the pieces of evidences are pooled by using Dempster's rule of combination. To identify the parameters involved in the overall and local models, the so-called imprecise training samples are constructed from running data by means of data analysis and expertise knowledge.To validate the proposed method, a numerical experiment was designed based on a UCI dataset, and the experimental results suggest its power for predicting evaluation of variables with uncertainty. Finally, the prediction of unmeasured parameter level of coal powder filling in tubular ball mill was taken as the engineering example to validate the proposed method. The predicting results are in line with the experts’ analysis. The two validations suggest that the proposed method is applicable for predicting variables with uncertainty in process industry.