A review of just‐in‐time learning‐based soft sensor in industrial process

Abstract Data‐driven soft sensing approaches have been a hot research field for decades and are increasingly used in industrial processes due to their advantages of easy implementation and high efficiency. However, nonlinear and time‐varying problems widely exist in practical industrial processes. Just‐in‐time learning (JITL) was proposed to solve these problems and has attracted great attention in practical applications. To present a comprehensive review of JITL‐based soft sensor studies and provide detailed technical guidance for new researchers, this paper introduces the recent research on JITL‐based soft sensor modelling methods in the industrial process from three aspects: similarity criterion, sample subset, and local model, which include the whole process of establishing a JITL‐based soft sensor. Moreover, the future research and innovation directions of JITL‐based soft sensors in industrial processes are also prospected.