Quality-Relevant and Process-Relevant Fault Monitoring with Concurrent Projection to Latent Structures

被引:218
|
作者
Qin, S. Joe [1 ]
Zheng, Yingying [1 ]
机构
[1] Univ So Calif, Dept Chem Engn & Mat Sci, Los Angeles, CA 90089 USA
关键词
concurrent projection to latent structures; process monitoring; quality monitoring; output-relevant fault detection; input-relevant fault detection; PRINCIPAL COMPONENT ANALYSIS; PARTIAL LEAST-SQUARES; IDENTIFICATION; DIAGNOSIS; RECONSTRUCTION; SENSORS; PLS;
D O I
10.1002/aic.13959
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
A new concurrent projection to latent structures for the monitoring of output-relevant faults that affect the quality and input-relevant process faults is proposed. The input and output data spaces are concurrently projected to five subspaces, a joint input-output subspace that captures covariations between input and output, an output-principal subspace, an output-residual subspace, an input-principal subspace, and an input-residual subspace. Fault detection indices are developed based on the concurrent projection to latent structures (CPLS) partition of subspaces for various fault detection alarms. The proposed CPLS monitoring method offers complete monitoring of faults that happen in the predictable output subspace and the unpredictable output-residual subspace, as well as faults that affect the input spaces and could be incipient for the output. Numerical simulation examples and the Tennessee Eastman challenge problem are used to illustrate the effectiveness of the proposed methods. (C) 2012 American Institute of Chemical Engineers AIChE J, 59: 496-504, 2013
引用
收藏
页码:496 / 504
页数:9
相关论文
共 50 条
  • [1] Parallel projection to latent structures for quality-relevant process monitoring
    Zheng, Ying
    Liu, Ziwei
    Yang, Weidong
    Tao, Bo
    Wan, Yanwei
    [J]. JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS, 2017, 80 : 76 - 84
  • [2] Improved Projection to Latent Structures for Quality-Relevant Process Monitoring
    Liu, Ziwei
    Zheng, Ying
    [J]. 2015 CHINESE AUTOMATION CONGRESS (CAC), 2015, : 875 - 879
  • [3] Quality-relevant and process-relevant fault diagnosis with concurrent modified partial least squares
    Li, Qiang
    Kong, Xiang-Yu
    Luo, Jia-Yu
    Xie, Jian
    [J]. Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2021, 38 (03): : 318 - 328
  • [4] Quality-relevant and process-relevant fault monitoring based on GNPER and the fault quantification index for industrial processes
    Mou, Miao
    Zhao, Xiaoqiang
    [J]. CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2023, 101 (02): : 967 - 983
  • [5] Sensitive Quality-Relevant Fault Monitoring using Enhanced Sparse Projection to Latent Structures
    Bai, Xiwei
    Wang, Xuelei
    Tan, Jie
    Qin, Wei
    Zhang, Tianren
    Sun, Wei
    [J]. 2018 13TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2018, : 687 - 693
  • [6] Recursive-CPLS-Based Quality-Relevant and Process-Relevant Fault Monitoring With Application to the Tennessee Eastman Process
    Hu, Changhua
    Xu, Zhongying
    Kong, Xiangyu
    Luo, Jiayu
    [J]. IEEE ACCESS, 2019, 7 : 128746 - 128757
  • [7] Quality-relevant fault monitoring based on efficient projection to latent structures with application to hot strip mill process
    Peng, Kaixiang
    Zhang, Kai
    You, Bo
    Dong, Jie
    [J]. IET CONTROL THEORY AND APPLICATIONS, 2015, 9 (07): : 1135 - 1145
  • [8] Teacher-Student Uncertainty Autoencoder for the Process-Relevant and Quality-Relevant Fault Detection in the Industrial Process
    Yang, Dan
    Peng, Xin
    Lu, Yusheng
    Huang, Haojie
    Zhong, Weimin
    [J]. IEEE Transactions on Artificial Intelligence, 2023, 4 (04): : 698 - 708
  • [9] Concurrent Projection to Latent Structures for Output-relevant and Input-relevant Fault Monitoring
    Qin, S. Joe
    Zheng, Yingying
    [J]. 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2012, : 7018 - 7023
  • [10] Quality-Relevant Fault Monitoring Based on Locally Linear Embedding Orthogonal Projection to Latent Structure
    Zhou, J. L.
    Ren, Y. W.
    Wang, J.
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2019, 58 (03) : 1262 - 1272