Development of correlation-based pattern recognition algorithm and adaptive soft-sensor design

Although soft-sensors have been used for estimating product quality, they do not always function well due to not only changes in process characteristics but also the individual difference of production devices. Correlation-based Just-In-Time (CoJIT) modeling has been proposed to cope with such changes in process characteristics; however it cannot deal with the individual difference. In the present work, a new pattern recognition method, referred to as the nearest correlation (NC) method is proposed to cope with the individual difference. The proposed NC method is integrated with CoJIT modeling. The advantages of the proposed methods are demonstrated through a case study.

Research highlights
► An amazing pattern recognition method based on correlation, instead of distance, was proposed.
► The nearest correlation method was integrated with Correlation-based Just-In-Time modeling.
► Our softsensors can cope with changes in process characteristics and individual difference.
► The prediction performance of NC-CoJIT is significantly better than that of conventional JIT.