An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

Abstract A novel approach named aligned mixture probabilistic principal component analysis (AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations, the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis (MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

[1]  R. Srinivasan,et al.  Immune-System-Inspired Approach to Process Monitoring and Fault Diagnosis , 2011 .

[2]  Shuai Li,et al.  Modeling and monitoring of nonlinear multi-mode processes , 2014 .

[3]  Jie Yu,et al.  A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis , 2012 .

[4]  Chunhui Zhao,et al.  Concurrent phase partition and between‐mode statistical analysis for multimode and multiphase batch process monitoring , 2014 .

[5]  Zhi-huan Song,et al.  Mixture Bayesian regularization method of PPCA for multimode process monitoring , 2010 .

[6]  Hongbo Shi,et al.  Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace , 2012 .

[7]  S. Qin,et al.  Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models , 2008 .

[8]  E. Lima,et al.  Modeling and performance monitoring of multivariate multimodal processes , 2013 .

[9]  Ahmet Palazoglu,et al.  Process pattern construction and multi-mode monitoring , 2012 .

[10]  Zhiqiang Ge,et al.  Maximum-likelihood mixture factor analysis model and its application for process monitoring , 2010 .

[11]  Jie Zhang,et al.  Performance monitoring of processes with multiple operating modes through multiple PLS models , 2006 .

[12]  Rajagopalan Srinivasan,et al.  Multi-model based process condition monitoring of offshore oil and gas production process , 2010 .

[13]  Yi Hu,et al.  Fault Detection and Identification Based on the Neighborhood Standardized Local Outlier Factor Method , 2013, Industrial & Engineering Chemistry Research.

[14]  S. Zhao,et al.  Monitoring of Processes with Multiple Operating Modes through Multiple Principle Component Analysis Models , 2004 .

[15]  Yee Whye Teh,et al.  Automatic Alignment of Local Representations , 2002, NIPS.

[16]  Seongkyu Yoon,et al.  Fault diagnosis with multivariate statistical models part I: using steady state fault signatures , 2001 .

[17]  Zhi-huan Song,et al.  Online monitoring of nonlinear multiple mode processes based on adaptive local model approach , 2008 .

[18]  Mengling Wang,et al.  Dynamic process monitoring using adaptive local outlier factor , 2013 .

[19]  H. Shi,et al.  Dynamic Multimode Process Modeling and Monitoring Using Adaptive Gaussian Mixture Models , 2012 .

[20]  Chonghun Han,et al.  Robust Recursive Principal Component Analysis Modeling for Adaptive Monitoring , 2006 .

[21]  Yingwei Zhang,et al.  Modeling and monitoring of multimode process based on subspace separation , 2013 .