Adaptive Gaussian Mixture Model-Based Relevant Sample Selection for JITL Soft Sensor Development

Just-in-time learning (JITL) has recently been used for online soft sensor modeling. Unlike traditional global approaches, the JITL-based method employs a local model built from historical samples similar to a query sample so that both nonlinearities and changes in process characteristics can be handled well. A key issue in JITL is to establish a suitable similarity criterion for selecting relevant samples. Conventional JITL methods, which use distance-based similarity measures for local modeling, can be inappropriate for many industrial processes exhibiting time-varying and non-Gaussian behaviors. In this article, a GMM-based similarity measure is proposed to improve the prediction accuracy of a JITL soft sensor. By taking the non-Gaussianity of the process data and the characteristics of the query sample into account, a more suitable similarity criterion is defined for sample selection of a JITL soft sensor, and better modeling performance can be achieved. Case studies involving a numerical example and an industrial process are discussed to demonstrate the feasibility and effectiveness of the proposed method.