Soft sensor design by multivariate fusion of image features and process measurements

This paper presents a multivariate data fusion procedure for design of dynamic soft sensors where suitably selected image features are combined with traditional process measurements to enhance the performance of data-driven soft sensors. A key issue of fusing multiple sensor data, i.e. to determine the weight of each regressor, is achieved through multivariate regression.

The framework is described and illustrated with applications to cement kiln systems that are characterized by off-line quality measurements and on-line analyzers with limited reliability. Image features are extracted with a multivariate analysis technique from RGB pictures. The color information is also transformed to hue, saturation and intensity components.

Both sets of image features are combined with traditional process measurements to obtain an inferential model by partial least squares (PLS) regression. A dynamic PLS model is obtained by filtering the original data block augmented with time lagged variables such that improved predictive performance of the quality variable results.

Key issues regarding data preprocessing and extraction of suitable image features are discussed with a case study, the on-line estimation of nitrogen oxides (NOx) emission of cement kilns. On-site tests demonstrate improved performance over soft sensors based on conventional process measurements only.