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Deep feature representation with online convolutional adversarial autoencoder for nonlinear process monitoring

JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2024年 155卷

作者： Yang, Xu Xiao, Jieshi Huang, Jian Peng, Kaixiang Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China

Background: The significant nonlinearity between the monitoring variables introduces challenges in the task of features extraction when implementing fault detection for an industrial process. Recently, neural network with complex hierarchical structure and layer-by-layer nonlinear transformation, especially autoencoder (AE), have attracted considerable attention from the process monitoring community. However, the latent features of AE cannot fully reflect process information, and there is redundancy between features. Methods: This study introduces an online convolutional adversarial autoencoder (AAE) model to learn nonlinear features with representative information of industrial processes. The structure of generative adversarial networks (GAN) in AAE aims to extract features that can reflect the manifold information and subject to the Gaussian distribution. Given the advantages of convolutional kernels in weight sharing and local perception, convolutional kernels are embedded in AAE to capture the spatial structure information of process data. On the basis of this model, the fault-relevant features selection strategy is designed to remove redundant information online and improve the accuracy of fault detection. Significant findings: The results show that the average fault detection rate of the penicillin fermentation process can be improved to 94% using the proposed algorithm comparing with the current fault detection methods.

关键词： nonlinear process monitoring Adversarial autoencoder Convolutional kernel Penicillin fermentation process

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KPCA-CCA-Based Quality-Related Fault Detection and Diagnosis Method for nonlinear process monitoring

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第5期19卷 6492-6501页

作者： Wang, Guang Yang, Jinghui Qian, Yucheng Han, Jingsong Jiao, Jianfang North China Elect Power Univ Dept Automat Baoding Campus Baoding 071003 Peoples R China

This work concerns the issue of quality-related fault detection and diagnosis (QrFDD) for nonlinear process monitoring. A kernel principal component analysis (KPCA)-based canonical correlation analysis (CCA) model is proposed in this article. First, KPCA is utilized to extract the kernel principal components (KPCs) of original variables data to eliminate nonlinear coupling among the variables. Then, the KPCs and output are used for CCA modeling, which not only avoids the complex decomposition of kernel CCA but also maintains high interpretability. Afterwards, under the premise of Gaussian kernel, a proportional relationship between process variables sample and kernel sample is introduced, on the basis of which, the linear regression model between process and quality variables is established. Based on the coefficient matrix of the regression model, a nonlinear QrFDD method is finally implemented which has both the data processing capability of nonlinear methods and the form of linear methods. Therefore, it significantly outperforms existing kernel-based CCA methods in terms of algorithmic complexity and interpretability, which is demonstrated by the simulation results of the Tennessee Eastman chemical process.

关键词： Kernel Correlation Informatics process monitoring Training Principal component analysis Matrix decomposition Quality-related canonical correlation analysis (CCA) fault diagnosis kernel principal component analysis (KPCA) nonlinear process monitoring

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A General Quality-Related nonlinear process monitoring Approach Based on Input-Output Kernel PLS

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Kong, Xiangyu Luo, Jiayu Feng, Xiaowei Liu, Meizhi High Tech Inst Xian Xian 710025 Peoples R China Shanxi Datong Univ Sch Phys & Elect Sci Datong 037009 Peoples R China

Projection to latent structure (PLS) is a well-known data-based approach widely used in industrial process monitoring. Kernel PLS (KPLS) was proposed in prior studies to apply the PLS in the nonlinear process. However, KPLS-based methods only consider the nonlinear variation of the input and ignore that of the input and output simultaneously. Once the nonlinearity lies in inputs and outputs, the KPLS-based methods cannot accurately describe the nonlinear feature, and result in missing alarms. To provide a common monitoring approach for various nonlinear cases, an input-output KPLS (IO-KPLS) model is proposed. The proposed IO-KPLS maps both the original input and output variables into a high-dimensional space. A new nonlinear objective function is then established to extract latent variables. In addition, a nonlinear regression is designed to construct the IO-KPLS model. By constructing statistics, a complete quality-related process monitoring strategy is designed. Driven by the proposed strategy, the nonlinear feature between input and output can be efficiently extracted, and a comprehensive monitoring performance is provided. A numerical example and two industrial benchmarks are performed to demonstrate the efficiency of the proposed method.

关键词： Feature extraction Kernel process monitoring Linear programming Data mining Principal component analysis Loading Data-driven fault detection kernel projection to latent structure (KPLS) nonlinear process monitoring quality-related

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

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INFORMATION SCIENCES 2022年 591卷 381-399页

作者： Yu, Jianbo Yan, Xuefeng East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

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.(c) 2021 Elsevier Inc. All rights reserved.

关键词： nonlinear process monitoring Static dynamic features sources Kernel slow feature analysis Stacked autoencoder RNN-LSTM autoencoder

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Artificial Neural Correlation Analysis for Performance-Indicator-Related nonlinear process monitoring

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第2期18卷 1039-1049页

作者： Chen, Qing Liu, Zhanzhan Ma, Xin Wang, Youqing Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

In this article, a novel fault detection and process monitoring method referred to as artificial neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are common in complex industrial processes, the classic canonical correlation analysis (CCA) always perform poorly. Many scholars have noticed the nonlinear problem of the process and have also proposed some improved schemes, such as the kernel method. However, the selection of suitable parameters in the kernel method is extremely difficult, so most of the kernel learning methods are slightly unsatisfactory. Considering that the artificial neural network (ANN) can well extract the required feature components from the nonlinear data, we combined ANN and CCA from their respective principles, and proposed a new nonlinear monitoring method and the detailed gradient descent method derivation for the ANCA network is presented. In addition, we have designed two indices to monitor the changes of process variables and performance indicators. Finally, a numerical example, the Tennessee Eastman benchmark, and the Zhoushan thermal power plant process illustrate the superiority of the proposed method.

关键词： Feature extraction Correlation Kernel Data mining process monitoring Fault detection Reactive power Artificial neural correlation analysis (ANCA) artificial neural network (ANN) canonical correlation analysis (CCA) fault detection nonlinear process monitoring

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nonlinear process monitoring based on decentralized generalized regression neural networks

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 150卷 113273-000页

作者： Lan, Ting Tong, Chudong Yu, Haizhen Shi, Xuhua Luo, Lijia Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Zhejiang Univ Technol Inst Proc Equipment & Control Engn Hangzhou 310014 Peoples R China

Given that the main task of process monitoring (i.e., fault detection) is actually a classical one-class classification problem, the generalized regression neural network (GRNN) is directly inapplicable for handling process modeling and monitoring issues. Through the selection of only one variable to be the output while the others serve as the corresponding input, a GRNN model can then be constructed to approximate the nonlinear input to output relationship. The residuals, signifying the inconsistency between the actual measurement and the predicted output from the GRNN model, could be a good indicator for online fault detection. The proposed nonlinear process monitoring approach is termed decentralized GRNN (DGRNN), which applies the GRNN in an extremely decentralized manner and utilizes the squared Mahalanobis distance for the online monitoring of the abnormalities captured by the generated residuals. The effectiveness and superiority of the DGRNN-based nonlinear process monitoring approach over other state-of-the-art nonlinear process monitoring methods are investigated by comparisons in two nonlinear processes. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Generalized regression neural network nonlinear process monitoring Fault detection Residual generation

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nonlinear process monitoring Based on Global Preserving Unsupervised Kernel Extreme Learning Machine

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IEEE ACCESS 2019年 7卷 106053-106064页

作者： Zhang, Hanyuan Deng, Xiaogang Zhang, Yunchu Hou, Chuanjing Li, Chengdong Xin, Zheng Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Shandong Peoples R China China Univ Petr East China Coll Informat & Control Engn Qingdao 266580 Shandong Peoples R China

Recently, the unsupervised extreme learning machine (UELM) technique as a nonlinear data mining approach has been employed to diagnose nonlinear process faults. However, during the dimensionality reduction of process observation data, UELM only aims at mining the local structure feature information of process data and lacks of the ability of preserving the global structure analysis of process data, which would perform unsatisfactorily for monitoring nonlinear process. Furthermore, the choice of the optimal number of hidden layer nodes in UELM method is still a challenging problem. To handle these two tough problems, a new fault detection approach on the basis of global preserving unsupervised kernel extreme learning machine (GUKELM) technique is proposed to monitor nonlinear process effectively. In GUKELM technique, the data global structure preserving framework is naturally incorporated into standard UELM to capture the local structure information as well as to preserve the global structure feature information of nonlinear process observation data during the dimensionality reduction. Meanwhile, to tackle the strong nonlinearity of process observation data, the kernel trick is utilized to successfully solve the challenging issue of setting the optimal number of hidden layer nodes. Based on extracted process data low dimensional feature information from GUKELM method, support vector data description algorithm is adopted to construct the monitoring statistic to detect process fault. Finally, the feasibility and effectiveness of the proposed process monitoring strategy is illustrated through a numerical nonlinear system and the continuous stirred tank reactor process which is a typical nonlinear process.

关键词： nonlinear process monitoring unsupervised extreme learning machine global structure analysis kernel trick support vector data description

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Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring

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JOURNAL OF process CONTROL 2019年 75卷 136-155页

作者： Zhang, Zehan Jiang, Teng Zhan, Chengjun Yang, Yupu Shanghai Jiao Tong Univ Dept Automat Minist Educ Syst Control & Informat Proc Key Lab Shanghai 200240 Peoples R China

Deep learning algorithms, especially the autoencoders, have been applied in nonlinear process monitoring recently. However, the features extracted by the autoencoders can hardly follow the Gaussian distribution, consequently, the control limit of the corresponding monitoring statistic can not be determined by an F or chi(2) distribution. Recent improvements in the unsupervised learning domain of deep learning offer opportunities to avoid the problem. In this paper, a novel nonlinear process monitoring method based on variational autoencoder (VAE) is proposed to tackle the Gaussian assumption problem. Due to the Gaussian distribution limitation added in the hidden layer of the VAE, it can not only automatically learn the key features of the nonlinear system, but also learn features that follow the Gaussian distribution. The Gaussian feature representations obtained from VAE are then provided to construct a new statistic H-2 whose control limit can be easily determined by a chi(2) distribution. A nonlinear numerical study and the TE benchmark process have verified the effectiveness of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： nonlinear process monitoring Deep learning Variational autoencoder Gaussian feature learning

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Learning Deep Correlated Representations for nonlinear process monitoring

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2019年第12期15卷 6200-6209页

作者： Jiang, Qingchao Yan, Xuefeng East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

Deep neural network (DNN) extracts hierarchical representations from process data and is promising for nonlinear process monitoring. Obtaining meaningful representations and generating efficient fault detection residual are the main challenges in DNN-based monitoring. This study proposes a regularized deep correlated representation (RDCR) method that incorporates deep belief networks (DBNs) and canonical correlation analysis (CCA) for nonlinear process monitoring. Hierarchical representations are initially extracted using DBN to process input and output variables. Second, hierarchical representations from process input and output are modeled through CCA to characterize the relationship between them. Efficient fault detection residuals are then generated, and monitoring statistics are established. CCA-based monitoring relies on the most correlated representations;thus, a multiobjective evolutionary optimization-based regularization is performed to select the most correlated representations and eliminate the influence of unrelated representations. The advantages of the RDCR monitoring are verified through experimental studies on a numerical example and the Tennessee Eastman process.

关键词： monitoring Feature extraction Optimization Correlation Fault detection Data mining Kernel Canonical correlation analysis (CCA) deep neural network (DNN) multiobjective evolutionary optimization (MEO) nonlinear process monitoring

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Improved nonlinear process monitoring based on ensemble KPCA with local structure analysis

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2019年 142卷 355-368页

作者： Cui, Ping Zhan, Chengjun Yang, Yupu Shanghai Jiao Tong Univ Dept Automat Minist Educ Syst Control & Informat Proc Key Lab Shanghai 200240 Peoples R China

A new nonlinear process monitoring algorithm called ensemble local kernel principal component analysis (ELKPCA) is proposed. Conventionally, the performance of kernel-based model depends on the width parameter selected empirically in Gaussian kernel function, which means a single parameter corresponds to a single model. Once a poor width parameter is decided, a single kernel-based model may be only effective for part faults. As a typical kernel-based method, the local kernel principal component analysis (LKPCA), considering both global and local structure information of the original data, faces the problem of width parameter selection as well. Since a single model is one-sided, an available way is to combine different single models and take advantage of them. The ensemble kernel principal component analysis (EKPCA) uses single KPCA models as its sub-models and ensemble learning approach is used to combine them. Due to inherit drawbacks from KPCA models, EKPCA only preserves global structure information of data, but ignores important local structure information. In this paper, to solve the above issues, both LKPCA and EKPCA is unified in the proposed framework. First, single LKPCA models are chosen instead and combined by using ensemble learning strategy. Then two monitoring statistics are turned into fault probabilities through Bayesian inference approach and weighted combination strategy, which makes the monitoring behavior easier and more clear. The result shows that ELKPCA model can not only take advantage of sub-LKPCA models effectively, allowing it selecting width parameter more easily and stably, but also retain both global and local structure information from input data by introducing the local structure analysis in the EKPCA model. Case studies on synthetic example and Tennessee Eastman process demonstrate the proposed method outperforms LKPCA and EKPCA and enhances monitoring performance significantly. (C) 2019 Institution of Chemical Engineers. Published b

关键词： nonlinear process monitoring Kernel ELKPCA Local structure analysis Fault detection

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