A Novel Decentralized Weighted ReliefF-PCA Method for Fault Detection
作者： Yinghua Yang , Xiangming Chen , Yue Zhang , Xiaozhi Liu
DOI: 10.1109/ACCESS.2019.2943024
关键词:
摘要: The decentralized weighted ReliefF-PCA (DWRPCA) method is proposed to improve the performance of principal component analysis (PCA) for fault detection. improved algorithm used select components instead traditional cumulative percent variance (CPV) criterion, so that important information contained in small considered. sub-models different types faults which are being considered influence weights process variables and established respectively obtain model. Bayesian Information Criterion adopted integrate a unified monitoring index. case study numerical example Tennessee Eastman illustrate effectiveness method.
sci-hub.se 参考文章(27)
Marko Robnik-Šikonja, Igor Kononenko, Theoretical and Empirical Analysis of ReliefF and RReliefF Machine Learning. ,vol. 53, pp. 23- 69 ,(2003) , 10.1023/A:1025667309714
Kenji Kira, Larry A. Rendell, A Practical Approach to Feature Selection international conference on machine learning. pp. 249- 256 ,(1992) , 10.1016/B978-1-55860-247-2.50037-1
Igor Kononenko, Estimating attributes: analysis and extensions of RELIEF european conference on machine learning. pp. 171- 182 ,(1994) , 10.1007/3-540-57868-4_57
Oscar Reyes, Carlos Morell, Sebastián Ventura, None, Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context Neurocomputing. ,vol. 161, pp. 168- 182 ,(2015) , 10.1016/J.NEUCOM.2015.02.045
Zhiqiang Ge, Zhihuan Song, Multimode process monitoring based on Bayesian method Journal of Chemometrics. ,vol. 23, pp. 636- 650 ,(2009) , 10.1002/CEM.1262
Weihua Li, S.Joe Qin, Consistent dynamic PCA based on errors-in-variables subspace identification Journal of Process Control. ,vol. 11, pp. 661- 678 ,(2001) , 10.1016/S0959-1524(00)00041-X
Carlos F. Alcala, S. Joe Qin, Reconstruction-based contribution for process monitoring Automatica. ,vol. 45, pp. 1593- 1600 ,(2009) , 10.1016/J.AUTOMATICA.2009.02.027
Weihua Li, H.Henry Yue, Sergio Valle-Cervantes, S.Joe Qin, Recursive PCA for adaptive process monitoring Journal of Process Control. ,vol. 10, pp. 471- 486 ,(2000) , 10.1016/S0959-1524(00)00022-6
Chunhui Zhao, Furong Gao, Fault-relevant Principal Component Analysis (FPCA) method for multivariate statistical modeling and process monitoring Chemometrics and Intelligent Laboratory Systems. ,vol. 133, pp. 1- 16 ,(2014) , 10.1016/J.CHEMOLAB.2014.01.009
Yingwei Zhang, Hong Zhou, S. Joe Qin, Tianyou Chai, Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares IEEE Transactions on Industrial Informatics. ,vol. 6, pp. 3- 10 ,(2010) , 10.1109/TII.2009.2033181