Cooperative Deep Dynamic Feature Extraction and Variable Time-Delay Estimation for Industrial Quality Prediction
Le Yao, Zhiqiang Ge
Abstract
In this article, a novel data-driven industrial quality predictor is proposed based on the cooperative deep dynamic feature extraction and variable time-delay (VTD) estimation. A semisupervised dynamic feature extracting (SSDFE) network is first proposed to extract nonlinear dynamic features to build a regression model for output quality prediction. Due to the inherent process structure and different positions of sampling instruments, time-delays commonly exist between process variables and quality variables, which may distort the original distribution and relationship in collected data. To recover the original process data pattern, the VTDs are regarded as model parameters and cooperatively obtained in the training process of the SSDFE network through an integer differential evolution algorithm. With the estimated VTD values, the reconstructed dataset further helps improve the prediction performance of the proposed SSDFE network. Two case studies are presented to demonstrate the superiority of the proposed method with VTD estimation.