Industrial Process Fault Monitoring Based on Adaptive Sliding Window-Recursive Sparse Principal Component Analysis

被引:0
作者
Liu J.-P. [1 ]
Wang J. [1 ]
Tang Z.-H. [2 ]
He J.-B. [1 ,4 ]
Xie Y.-F. [2 ]
Ma T.-Y. [1 ,3 ]
机构
[1] Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, Hunan
[2] School of Automation, Central South University, Changsha, 410083, Hunan
[3] School of Physics and Electronics, Hunan Normal University, Changsha, 410081, Hunan
[4] Hunan Institute of Metrology and Test, Changsha, 410014, Hunan
来源
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 09期
关键词
Fault monitoring; Recursive sparse principal component analysis(RSPCA); Sliding window; Time-varying industrial processes;
D O I
10.3969/j.issn.0372-2112.2020.09.018
中图分类号
学科分类号
摘要
This paper presents an adaptive sliding window recursive sparse principal component analysis method for the on-line fault monitoring of time-varying industrial processes. Firstly, feature information of normal process data space is extracted by the sliding window, and the sparse principal component analysis is applied to the current window block matrix to construct the sparse principal component analysis-based process fault monitoring model. Then, the forgetting factor is adjusted in real time according to the similarities of adjacent windows to update the sliding window size adaptively, so that the sparse principal component fault monitoring model can effectively track the time-varying process. Finally, the sparse load matrix of the sliding window is renewed recursively to update the fault monitoring model dynamically. Fault monitoring results of the nonlinear numerical simulation system and the Tennessee-Eastman process show that the proposed method can effectively improve the fault detection accuracy and adapt to the on-line fault monitoring of long process industries with time-varying processes. © 2020, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码:1795 / 1803
页数:8
相关论文
共 17 条
  • [1] Zheng J, Pan H, Qi X, Et al., Enhanced empirical wavelet transform based time-frequency analysis and its application to rolling bearing fault diagnosis, Acta Electronica Sinica, 46, 2, pp. 358-364, (2018)
  • [2] Zhang D, Wang X, Huang G., Check valve fault diagnosis with correlation coefficient SVD enhanced stochastic resonance, Acta Electronica Sinica, 46, 11, pp. 2696-2704, (2018)
  • [3] Li W, Peng M, Wang Q., Fault detectability analysis in PCA method during condition monitoring of sensors in a nuclear power plant, Annals of Nuclear Energy, 119, 9, pp. 342-351, (2018)
  • [4] Ait-Izem T, Harkat M F, Djeghaba M, Et al., On the application of interval PCA to process monitoring: A robust strategy for sensor FDI with new efficient control statistics, Journal of Process Control, 63, 1, pp. 29-46, (2018)
  • [5] Zou H, Hastie T, Tibshirani R., Sparse principal component analysis, Journal of Computational & Graphical Statistics, 15, 2, pp. 265-286, (2006)
  • [6] Sch Lkopf B, Smola A, LLER M, Nonlinear component analysis as a kernel eigenvalue problem, Neural Computation, 10, 5, pp. 1299-1319, (1998)
  • [7] Lee J M, Yoo C K, Lee I B., Statistical process monitoring with independent component analysis, Journal of Process Control, 14, 5, pp. 467-485, (2004)
  • [8] Qui Ones-Grueiro M, Prieto-Moreno A, Verde C, Et al., Data-driven monitoring of multimode continuous processes: A review, Chemometrics and Intelligent Laboratory Systems, 189, 1, pp. 56-71, (2019)
  • [9] WANG B, LI H X., A sliding window based dynamic spatiotemporal modeling for distributed parameter systems with time-dependent boundary conditions, IEEE Transactions on Industrial Informatics, 15, 4, pp. 2044-2053, (2019)
  • [10] Liu X, Kruger Uwe, Et al., Moving window kernel PCA for adaptive monitoring of nonlinear processes, Chemometrics & Intelligent Laboratory Systems, 96, 2, pp. 132-143, (2009)