Data-driven Online Operating Performance Assessment for Multi-Datasets Multivariable Industrial Processes

Abstract

In this study, a novel online operating performance assessment method based on multi-sets two-step basis vector extraction artificial neural networks (MTBVE-ANN) strategy is proposed for industrial applications. The MTBVE-ANN method focuses on finding common and specific information involved in multi-datasets, which improves the accuracy of data nonlinear characterization with artificial neural networks introduced. The optimality related variations are extracted from each operating performance grade by analysing the common and unique variations over online steady performance grades. The online operating performance assessment method is performed based on the similarities between the optimality related variations of the test data and that of historical training data. Previously, total projection to latent structures (T-PLS) operating performance assessment method must be performed based on the availability of both input and output data. The proposed method in this paper takes the artificial neural network to assess the operating performance grade of the online test data without output. The validity and precision of the proposed operating performance assessment method is illustrated with the industrial data of multi-datasets multivariable industrial processes.