A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 8
Aug.  2021

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Donglei Zheng, Le Zhou and Zhihuan Song, "Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process," IEEE/CAA J. Autom. Sinica, vol. 8, no. 8, pp. 1465-1476, Aug. 2021. doi: 10.1109/JAS.2021.1004090
Citation: Donglei Zheng, Le Zhou and Zhihuan Song, "Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process," IEEE/CAA J. Autom. Sinica, vol. 8, no. 8, pp. 1465-1476, Aug. 2021. doi: 10.1109/JAS.2021.1004090

Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process

doi: 10.1109/JAS.2021.1004090
Funds:  This work was supported by Zhejiang Provincial Natural Science Foundation of China (LY19F030003), Key Research and Development Project of Zhejiang Province (2021C04030), the National Natural Science Foundation of China (62003306) and Educational Commission Research Program of Zhejiang Province (Y202044842)
More Information
  • In practical process industries, a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes, which indicates that the measurements coming from different sources are collected at different sampling rates. To build a complete process monitoring strategy, all these multi-rate measurements should be considered for data-based modeling and monitoring. In this paper, a novel kernel multi-rate probabilistic principal component analysis (K-MPPCA) model is proposed to extract the nonlinear correlations among different sampling rates. In the proposed model, the model parameters are calibrated using the kernel trick and the expectation-maximum (EM) algorithm. Also, the corresponding fault detection methods based on the nonlinear features are developed. Finally, a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.

     

  • loading
  • [1]
    S. J. Qin, “Survey on data-driven industrial process monitoring and diagnosis,” Annual Reviews in Control, vol. 36, no. 2, pp. 220–234, 2012. doi: 10.1016/j.arcontrol.2012.09.004
    [2]
    Q. Liu, T. Y. Chai, S. J. Qin, and J. Zhao, “Progress of data-driven and knowledge-driven process monitoring and fault diagnosis for industry process,” Control and Decision, vol. 25, no. 6, pp. 801–807, 2010.
    [3]
    Z. Ge, Z. Song, and F. Gao, “Review of Recent Research on Data-Based Process Monitoring,” Industrial &Engineering Chemistry Research, vol. 52, no. 10, pp. 3543–3562, 2013.
    [4]
    4. S Yin, S. Ding, X. Xie, and H. Luo, “A review on basic data-driven approaches for industrial process monitoring,” IEEE Trans. Industrial Electronics, vol. 61, no. 11, pp. 6418–6428, 2014. doi: 10.1109/TIE.2014.2301773
    [5]
    W. Li, H. Yue, S, Valle-Cervantes, and S. J. Qin, “Recursive PCA for adaptive process monitoring,” Journal of Process Control, vol. 10, no. 5, pp. 471–486, 2000. doi: 10.1016/S0959-1524(00)00022-6
    [6]
    S. Yin, S. Ding, A. Haghani, H. Hao, and P. Zhang, “A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process,” Journal of Process Control, vol. 22, no. 9, pp. 1567–1581, 2012. doi: 10.1016/j.jprocont.2012.06.009
    [7]
    W. Woodall and D. C. Montgomery, “Some current directions in the theory and application of statistical process monitoring,” Journal of Quality Technology, vol. 46, no. 1, pp. 78–94, 2014. doi: 10.1080/00224065.2014.11917955
    [8]
    S. J. Qin, “Statistical process monitoring: basics and beyond,” Journal of Chemometrics, vol. 17, no. 8–9, pp. 480–502, 2003. doi: 10.1002/cem.800
    [9]
    J. F. Macgregor, C. Jaeckle, C. Kiparissides, and M. Koutoudi, “Process monitoring and diagnosis by multiblock PLS methods,” AIChE Journal, vol. 40, no. 5, pp. 826–838, 1994. doi: 10.1002/aic.690400509
    [10]
    J. Chen and K. C. Liu, “On-line batch process monitoring using dynamic PCA and dynamic PLS models,” Chemical Engineering Science, vol. 57, no. 1, pp. 63–75, 2002. doi: 10.1016/S0009-2509(01)00366-9
    [11]
    G. Li, S. J. Qin, and D. Zhou, “Geometric properties of partial least squares for process monitoring,” Automatica, vol. 46, no. 1, pp. 204–210, 2010. doi: 10.1016/j.automatica.2009.10.030
    [12]
    J. Huang, S. Yan, and X. Yan, “Robust chemical process monitoring based on CDC-MVT-PCA eliminating outliers and optimally selecting principal component,” Canadian Journal of Chemical Engineering, vol. 97, no. 6, pp. 1848–1857, 2019. doi: 10.1002/cjce.23437
    [13]
    Z. Ge, “Process data analytics via probabilistic latent variable models: A tutorial review,” Industrial &Engineering Chemistry Research, vol. 57, no. 38, pp. 12646–12661, 2018.
    [14]
    Z. Ge and Z. Song, “Maximum-likelihood mixture factor analysis model and its application for process monitoring,” Chemometrics &Intelligent Laboratory Systems, vol. 102, no. 1, pp. 53–61, 2010.
    [15]
    J. Zheng, L. Zhou, Z. Ge, and Z. Song, “Switching autoregressive dynamic latent variable model for fault detection in multimode processes. Data Driven Control and Learning Systems”, in Proc. 6th Data Driven Control and Learning Systems (DDCLS), 2017, pp. 617–622.
    [16]
    l. Zhou, J. Zheng, Z. Ge, Z. Song, and S. Shan, “Multimode process monitoring based on switching autoregressive dynamic latent variable model,” IEEE Trans. Industrial Electronics, vol. 65, no. 10, pp. 8184–8194, 2018. doi: 10.1109/TIE.2018.2803727
    [17]
    C. Chen, Z. Liu, W. H. Lin, S. Li and K. Wang, “Distributed modeling in a mapreduce framework for data-driven traffic flow forecasting,” IEEE Trans. Intelligent Transportation Systems, vol. 14, no. 1, pp. 22–33, 2013. doi: 10.1109/TITS.2012.2205144
    [18]
    Z. Ge, Z. Song, S. X. Ding, and B. Huang, “Data mining and analytics in the process industry: the role of machine learning,” IEEE Access, vol. 5, pp. 20590–20616, 2017. doi: 10.1109/ACCESS.2017.2756872
    [19]
    S. Yin and O. Kaynak, “Big data for moden industy: Challenges and trends,” IEEE Trans. Industrial Electronics, vol. 103, no. 2, pp. 143–146, 2015.
    [20]
    N. Lu, Y. Yang, F. Ga, and F. Wang, “Multirate dynamic inferential modeling for multivariable processes,” Chemical Engineering Science, vol. 59, no. 4, pp. 855–864, 2004. doi: 10.1016/j.ces.2003.12.003
    [21]
    O. Marjanovic, B. Lennox, D. Sandoz, K. Smith, and M. Crofts, “Real-time monitoring of an industrial batch process,” Computers &Chemical Engineering, vol. 30, no. 10–12, pp. 1476–1481, 2006.
    [22]
    D. Li, S.L Shah, and T. Chen, “Identification of fast-rate models from multirate data,” Int. Journal of Control, vol. 74, no. 7, pp. 680–689, 2001. doi: 10.1080/00207170010018904
    [23]
    M. E. Tipping and C. M. Bishop, “Probabilistic principal component analysis,” Journal of the Royal Statistical Society:Series B (Statistical Methodology), vol. 61, no. 3, pp. 611–622, 1999. doi: 10.1111/1467-9868.00196
    [24]
    Z. Ge, B. Huang, and Z. Song, “Mixture semi-supervised principal component regression model and soft sensor application,” American Institute of Chemical Engineers Journal, vol. 60, no. 2, pp. 533–545, 2014. doi: 10.1002/aic.14270
    [25]
    L. Zhou, J. Chen, Z. Song, Z. Ge, and A. Miao, “Probabilistic latent variable regression model for process-quality monitoring,” Chemical Engineering Science, vol. 116, pp. 296–305, 2014. doi: 10.1016/j.ces.2014.04.045
    [26]
    L. Zhou, J. Chen, J. Jie, and Z. Song, “Multiple probability principal component analysis for process monitoring with multi-rate measurements,” Journal of the Taiwan Institute of Chemical Engineers, vol. 96, pp. 18–28, 2018.
    [27]
    L. Zhou, J. Chen, J. Jie, and Z. Song, “Multirate Factor Analysis Models for Fault Detection in Multirate Processes,” IEEE Trans. Industrial Informatics, vol. 15, no. 7, pp. 4076–4085, 2019. doi: 10.1109/TII.2018.2889750
    [28]
    C. M. Bishop, Pattern Recognition and Machine Learning. New York, USA: Springer, 2006.
    [29]
    Z. Ge, C. Yang, and Z. Song, “Improved kernel-PCA based monitoring approach for nonlinear processes,” Chemical Engineering Science, vol. 64, pp. 2245–2255, 2009. doi: 10.1016/j.ces.2009.01.050
    [30]
    Z. Ge and Z. Song, “Kernel Generalization of PPCA for Nonlinear Probabilistic Monitoring,” Industrial &Engineering Chemistry Research, vol. 49, pp. 11832–11836, 2010.
    [31]
    L. Zhou, Z. Song, B. Hou, and Z. Fei, “Robust semi-supervised modeling method and its application to fault detection in chemical processes,” Journal of Chemical Industry and Engineering (China), vol. 68, no. 3, pp. 1109–1115, 2017.
    [32]
    J. Huang and X. Yan, “Quality-Driven Principal Component Analysis Combined with Kernel Least Squares for Statistical Process Monitoring,” IEEE Trans. Control Systems Technology, vol. 27, no. 6, pp. 2688–2695, 2019. doi: 10.1109/TCST.2018.2865130
    [33]
    Z. Li and X. Yan, “Ensemble model of wastewater treatment plant based on rich diversity of principal component determining by genetic algorithm for status monitoring,” Control Engineering Practice, vol. 88, pp. 38–51, 2019. doi: 10.1016/j.conengprac.2019.04.008
    [34]
    Z. Li and X. Yan, “Ensemble learning model based on selected diverse principal components analysis models for process monitoring”, Journal of Chemometrics, vol.32, no.6, Article No. e3010, 2018.
    [35]
    M. Sohaib, C. H. Kim, and J. M. Kim, “A hybrid feature model and deep-learning-based bearing fault diagnosis”, Sensors, vol.17, no.12, Article No. 2876, 2017.
    [36]
    L. Jiang, Z. Song, Z. Ge, and J. Chen, “Robust Self-Supervised Model and Its Application for Fault Detection,” Industrial &Engineering Chemistry Research, vol. 56, pp. 7503–7515, 2017.
    [37]
    X. Deng, X. Tian, S. Chen, and C. J. Harris, “Deep learning based nonlinear principal component analysis for industrial process fault detection”, in Proc. Int. Joint Conf. Neural Networks (IJCNN), 2017, pp.1237–1243.
    [38]
    H. Wu, J. Zhao, “Deep convolutional neural network model based chemical process fault diagnosis,” Computers &Chemical Engineering, vol. 115, pp. 185–197, 2018.
    [39]
    G. E. Box, “Some theorems on quadratic forms applied in the study of analysis of variance problems (Ⅰ): Effect of inequality of variance in the one-way classification,” Annals of Mathematical Statistics, vol. 25, no. 2, pp. 290–302, 1954. doi: 10.1214/aoms/1177728786

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(4)

    Article Metrics

    Article views (730) PDF downloads(45) Cited by()

    Highlights

    • The constraint nonlinear relationships with different sampling rates are considered in the proposed model.
    • A nonlinear EM algorithm is proposed for model parameter estimation.
    • A fault detection scheme for a nonlinear multi-rate process is developed.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return