Data-feature-driven nonlinear process monitoring based on joint deep learning models with dual-scale

The interactions among the gauged data in most exiting real-life cases are correlative inevitably given the complicated behavior of process systems, that is the observed input data should better be interpreted as generating from joint interaction of static and dynamic feature sources. Therefore, the...

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Bibliographic Details
Published in:Information sciences Vol. 591; pp. 381 - 399
Main Authors: Yu, Jianbo, Yan, Xuefeng
Format: Journal Article
Language:English
Published: Elsevier Inc 01.04.2022
Subjects:
Kernel slow feature analysis
Nonlinear process monitoring
RNN-LSTM autoencoder
Stacked autoencoder
Static/dynamic features sources
Nonlinear process monitoring
Kernel slow feature analysis
Static/dynamic features sources
Stacked autoencoder
RNN-LSTM autoencoder
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:The interactions among the gauged data in most exiting real-life cases are correlative inevitably given the complicated behavior of process systems, that is the observed input data should better be interpreted as generating from joint interaction of static and dynamic feature sources. Therefore, the traditional single static-based or dynamic-based methods will inevitably lose some of the important information features, which will result in unsatisfactory monitoring results. Motivated by this, we expect to divide the data into static and dynamic features to perform separate modeling. Hence, the kernel slow feature analysis method is proposed to achieve this purpose. The output from the KSFA model can be divided into static and dynamic sources according to the dynamic characteristics. Then, in order to extract the unsupervised dynamic features, a novel deep RNN-LSTM autoencoder model is developed, which includes three components: data conversion, deep model construction, and feature removal. Moreover, we use a stacked autoencoder for static sources to learn static features and construct high-order models for fault detection. Based on the well-built detection system formed by the joint deep models, dual-scale decision-making is integrated by the Bayesian inference method. Finally, the superiority of the proposal can be shown in two processes.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2021.12.106