Fault isolation using modified contribution plots
暂无分享,去创建一个
[1] Seongkyu Yoon,et al. Statistical and causal model‐based approaches to fault detection and isolation , 2000 .
[2] Chunhui Zhao,et al. A multiple-time-region (MTR)-based fault subspace decomposition and reconstruction modeling strategy for online fault diagnosis , 2012 .
[3] Tao Chen,et al. A branch and bound method for isolation of faulty variables through missing variable analysis , 2010 .
[4] S. Joe Qin,et al. Subspace approach to multidimensional fault identification and reconstruction , 1998 .
[5] Theodora Kourti,et al. Multivariate SPC Methods for Process and Product Monitoring , 1996 .
[6] Dale E. Seborg,et al. Pattern Matching in Multivariate Time Series Databases Using a Moving-Window Approach , 2002 .
[7] S. Joe Qin,et al. Reconstruction-Based Fault Identification Using a Combined Index , 2001 .
[8] Age K. Smilde,et al. Generalized contribution plots in multivariate statistical process monitoring , 2000 .
[9] Si-Zhao Joe Qin,et al. Reconstruction-based contribution for process monitoring , 2009, Autom..
[10] Svante Wold,et al. Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection , 1996 .
[11] S. Joe Qin,et al. Statistical process monitoring: basics and beyond , 2003 .
[12] Jialin Liu,et al. Fault Detection and Identification Using Modified Bayesian Classification on PCA Subspace , 2009, Industrial & Engineering Chemistry Research.
[13] J. E. Jackson. A User's Guide to Principal Components , 1991 .
[14] Michael J. Piovoso,et al. On unifying multiblock analysis with application to decentralized process monitoring , 2001 .
[15] S. Qin,et al. Self-validating inferential sensors with application to air emission monitoring , 1997 .
[16] Seongkyu Yoon,et al. Fault diagnosis with multivariate statistical models part I: using steady state fault signatures , 2001 .