An adaptive strategy for time-varying batch process fault prediction based on stochastic configuration network

Abstract

Fault prediction ensures safe and stable production, and cuts maintenance costs. Due to the changing operating conditions that lead to the changes in the characteristics of industrial processes, there is a need to monitor the fault state of batch processes in real-time and to accurately predict fault trends. An adaptive slow feature analysis-neighborhood preserving embedding-improved stochastic configuration network (SFA-NPE-ISCN) algorithm for batch process fault prediction is proposed. Firstly, SFA is used to extract the time-varying features of process data and establish the update index of the NPE model. Then, to extract local nearest-neighbor features and reconstruct them by the NPE model with adaptive update capability, square prediction error (SPE) statistics are constructed as fault state features based on the reconstructed error. Further, the hunter-prey optimization (HPO) algorithm optimizes the weights and biases in the stochastic configuration network, and the singular value decomposition (SVD) and QR decomposition of column rotation are introduced to solve the ill-posed problem of SCN and obtain the prediction model of ISCN. Finally, the obtained statistics SPE is formed into a time series, and the ISCN model is used to predict the process state trend. The effectiveness of the proposed algorithm is verified by case studies of industrial-scale penicillin fermentation processes and the Hot strip mill process.