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Gao H, Huang W, Gao X, Han H. Decentralized adaptively weighted stacked autoencoder-based incipient fault detection for nonlinear industrial processes. ISA TRANSACTIONS 2023; 139:216-228. [PMID: 37202232 DOI: 10.1016/j.isatra.2023.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 02/24/2023] [Accepted: 04/28/2023] [Indexed: 05/20/2023]
Abstract
Modern industrial processes often exhibit large-scale and nonlinear characteristics. Incipient fault detection for industrial processes is a big challenge because of the faint fault signature. To improve the performance of incipient fault detection for large-scale nonlinear industrial processes, a decentralized adaptively weighted stacked autoencoder (DAWSAE) -based fault detection method is proposed. First, the industrial process is divided into several sub-blocks and local adaptively weighted stacked autoencoder (AWSAE) is established for each sub-block to mine local information and obtain local adaptively weighted feature vectors and residual vectors. Second, the global AWSAE is established for the whole process to mine global information and obtain global adaptively weighted feature vectors and residual vectors. Finally, local statistics and global statistics are constructed based on local and global adaptively weighted feature vectors and residual vectors to detect the sub-blocks and the whole process, respectively. The advantages of proposed method are verified by a numerical example and Tennessee Eastman process (TEP).
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Affiliation(s)
- Huihui Gao
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China; Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China; Beijing Artificial Intelligence Institute, China
| | - Wenjie Huang
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China; Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China; Beijing Artificial Intelligence Institute, China
| | - Xuejin Gao
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China; Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China; Beijing Artificial Intelligence Institute, China
| | - Honggui Han
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China; Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China; Beijing Artificial Intelligence Institute, China.
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Bernal-de-Lázaro JM, Cruz-Corona C, Silva-Neto AJ, Llanes-Santiago O. Criteria for optimizing kernel methods in fault monitoring process: A survey. ISA TRANSACTIONS 2022; 127:259-272. [PMID: 34511263 DOI: 10.1016/j.isatra.2021.08.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 08/27/2021] [Accepted: 08/29/2021] [Indexed: 06/13/2023]
Abstract
Nowadays, how to select the kernel function and their parameters for ensuring high-performance indicators in fault diagnosis applications remains as two open research issues. This paper provides a comprehensive literature survey of kernel-preprocessing methods in condition monitoring tasks, with emphasis on the procedures for selecting their parameters. Accordingly, twenty kernel optimization criteria and sixteen kernel functions are analyzed. A kernel evaluation framework is further provided for helping in the selection and adjustment of kernel functions. The proposal is validated via a KPCA-based monitoring scheme and two well-known benchmark processes.
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Affiliation(s)
- José M Bernal-de-Lázaro
- Department of Automation and Computing, Universidad Tecnológica de La Habana "José Antonio Echeverría", CUJAE, Cuba
| | - Carlos Cruz-Corona
- Department of Computer Science and Artificial Intelligence, University of Granada, Spain
| | - Antônio J Silva-Neto
- Department of Mechanical Engineering, Universidade do Estado do Rio de Janeiro, IPRJ-UERJ, RJ, Brazil
| | - Orestes Llanes-Santiago
- Department of Automation and Computing, Universidad Tecnológica de La Habana "José Antonio Echeverría", CUJAE, Cuba.
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Cai P, Deng X. Incipient fault detection for nonlinear processes based on dynamic multi-block probability related kernel principal component analysis. ISA TRANSACTIONS 2020; 105:210-220. [PMID: 32466844 DOI: 10.1016/j.isatra.2020.05.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 05/02/2023]
Abstract
In order to detect the incipient faults of nonlinear industrial processes effectively, this paper proposes an enhanced kernel principal component analysis (KPCA) method, called multi-block probability related KPCA method (DMPRKPCA). First of all, one probability related nonlinear statistical monitoring framework is constructed by combining KPCA with Kullback Leibler divergence (KLD), which measures the probability distribution changes caused by small shifts. Second, in view of the problem that the traditional KLD ignores the dynamic characteristic of process data, the dynamic KLD component is designed by applying the exponentially weighted moving average approach, which highlights the temporal data changes in the moving window. Third, considering that the holistic KLD component may submerge the local statistical changes, a multi-block modeling strategy is designed by dividing the whole KLD components into two sub-blocks corresponding to the mean and variance information, respectively. Case studies on one numerical system and the simulated chemical reactor demonstrate the superiority of the DMPRKPCA method over the conventional KPCA method.
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Affiliation(s)
- Peipei Cai
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China.
| | - Xiaogang Deng
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China.
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Deng X, Cai P, Cao Y, Wang P. Two-Step Localized Kernel Principal Component Analysis Based Incipient Fault Diagnosis for Nonlinear Industrial Processes. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b06826] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Xiaogang Deng
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China
| | - Peipei Cai
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China
| | - Yuping Cao
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China
| | - Ping Wang
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China
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Yuan J, Wang S, Wang F, Zhang S. Abnormal Condition Identification via OVR-IRBF-NN for the Process Industry with Imprecise Data and Semantic Information. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b06554] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jie Yuan
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Shu Wang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
| | - Fuli Wang
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
- State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
| | - Shumei Zhang
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
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Mann V, Maurya D, Tangirala AK, Narasimhan S. Optimal Filtering and Residual Analysis in Errors-in-Variables Model Identification. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b04561] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Wan X, Tong C, Luo L. Distributed Statistical Process Monitoring Based on Multiblock Canonical Correlation Analysis. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b04971] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Xinchun Wan
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, P. R. China
| | - Chudong Tong
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, P. R. China
| | - Lijia Luo
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
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A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring. Processes (Basel) 2019. [DOI: 10.3390/pr8010024] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.
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Cong L, Liu X. Temperature Inferential Control of Heat‐Integrated Distillation Column Based on Variable Sensitive Stage Temperature Set‐point. CAN J CHEM ENG 2019. [DOI: 10.1002/cjce.23527] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Lin Cong
- College of Information and Control EngineeringChina University of Petroleum (East China) Qingdao China
| | - Xinggao Liu
- Institute of Industrial Process Control, Department of Control Science and EngineeringZhejiang University Hangzhou China
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Wang C, He F. State Clustering of the Hot Strip Rolling Process via Kernel Entropy Component Analysis and Weighted Cosine Distance. ENTROPY 2019. [PMCID: PMC7514240 DOI: 10.3390/e21101019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the hot strip rolling process, many process parameters are related to the quality of the final products. Sometimes, the process parameters corresponding to different steel grades are close to, or even overlap, each other. In reality, locating overlap regions and detecting products with abnormal quality are crucial, yet challenging. To address this challenge, in this work, a novel method named kernel entropy component analysis (KECA)-weighted cosine distance is introduced for fault detection and overlap region locating. First, KECA is used to cluster the training samples of multiple steel grades, and the samples with incorrect classes are seen as the boundary of the sample distribution. Next, the concepts of recursive-based regional center and weighted cosine distance are introduced. For each steel grade, the regional center and the weight coefficients are determined. Finally, the weighted cosine distance between the testing sample and the regional center is chosen as the index to judge abnormal batches. The samples in the overlap region of multiple steel grades need to be focused on in the real production process, which is conducive to quality grade and combined production. The weighted cosine distances between the testing sample and different regional centers are used to locate the overlap region. A dataset from a hot steel rolling process is used to evaluate the performance of the proposed methods.
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