Rethinking the Value of Just-In-Time Learning in the Era of Industrial Big Data

Abstract

Just-in-time learning (JITL) has become a widely used industrial process modeling tool. With the advent of the industrial big data era, rich data information has brought new opportunities to JITL. Specifically, the completeness of data samples in the era of big data provides an important premise and support for the JITL method, prompting us to rethink the application value of JITL in the context of industrial big data. In this paper, a parallel computing strategy is adopted to divide the entire computational searching task into several subtasks, and assign the subtasks to parallel computing nodes to complete parallel searching. In addition, in order to improve the real-time nature of JITL, a model library management (MLM) strategy is adopted. And by selectively adding new data, the database management (DBM) strategy is also developed, which not only alleviates the problem of information redundancy, but also reduces the search pressure caused by the increasingly large database. Combining parallel computing, a parallel JITL (P-JITL) framework is proposed. As an example, the variational Bayesian factor regression (VBFR) model is transformed into the parallel Bayesian-JITL (PB-JITL) method for big process data modeling. To evaluate the feasibility and efficiency of the developed methods, a real industrial case is demonstrated.