Multi-layer Contribution Propagation Analysis for Fault Diagnosis

The recent development of feature extraction algorithms with multiple layers in machine learning and pattern recognition has inspired many applications in multivariate statistical process monitoring. In this work, two existing multilayer linear approaches in fault detection are reviewed and a new one with extra layer is proposed in analogy. To provide a general framework for fault diagnosis in succession, this work also proposes the contribution propagation analysis which extends the original definition of contribution of variables in multivariate statistical process monitoring. In fault diagnosis stage, the proposed contribution propagation analysis for multilayer linear feature extraction algorithms is compared with the fault diagnosis results of original contribution plots associated with single layer feature extraction approach. Plots of variable contributions obtained by the aforementioned approaches on the data sets collected from a simulated benchmark case study (Tennessee Eastman process) as well as an industrial scale multiphase flow facility are presented as a demonstration of the usage and performance of the contribution propagation analysis on multilayer linear algorithms.

[1]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[2]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[3]  Zhiqiang Ge,et al.  Semisupervised Kernel Learning for FDA Model and its Application for Fault Classification in Industrial Processes , 2016, IEEE Transactions on Industrial Informatics.

[4]  S. Joe Qin,et al.  Reconstruction-based Contribution for Process Monitoring , 2008 .

[5]  S. Joe Qin,et al.  Multivariate process monitoring and fault diagnosis by multi-scale PCA , 2002 .

[6]  Mohamed Benouaret,et al.  Sensor fault detection, isolation and reconstruction using nonlinear principal component analysis , 2007, Int. J. Autom. Comput..

[7]  Yi Cao,et al.  Contribution plots based fault diagnosis of a multiphase flow facility with PCA-enhancec canonical variate analysis , 2017, 2017 23rd International Conference on Automation and Computing (ICAC).

[8]  Fuli Wang,et al.  Adaptive Monitoring Based on Independent Component Analysis for Multiphase Batch Processes with Limited Modeling Data , 2008 .

[9]  Xiaoguang Zhao,et al.  PLS-CCA heterogeneous features fusion-based low-resolution human detection method for outdoor video surveillance , 2017, Int. J. Autom. Comput..

[10]  Chunhui Zhao,et al.  Nonlinear Batch Process Monitoring Using Phase-Based Kernel-Independent Component Analysis-Principal Component Analysis (KICA-PCA) , 2009 .

[11]  F. V. D. van den Berg,et al.  Confidence limits for contribution plots in multivariate statistical process control using bootstrap estimates. , 2016, Analytica chimica acta.

[12]  Zhi-huan Song,et al.  Process Monitoring Based on Independent Component Analysis - Principal Component Analysis ( ICA - PCA ) and Similarity Factors , 2007 .

[13]  Dexian Huang,et al.  Canonical variate analysis-based contributions for fault identification , 2015 .

[14]  Yi Cao,et al.  Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

[15]  Jin Hyun Park,et al.  Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis , 2004, Comput. Chem. Eng..

[16]  Yi Cao,et al.  Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations , 2010, IEEE Transactions on Industrial Informatics.

[17]  Jianxin Zhou,et al.  A new reconstruction-based auto-associative neural network for fault diagnosis in nonlinear systems , 2018 .

[18]  Cristobal Ruiz-Carcel,et al.  Statistical process monitoring of a multiphase flow facility , 2015 .

[19]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[20]  Age K. Smilde,et al.  Generalized contribution plots in multivariate statistical process monitoring , 2000 .

[21]  Y. Cao,et al.  State-space independent component analysis for nonlinear dynamic process monitoring , 2010 .

[22]  Xin Ye,et al.  A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: A case study on cryogenic propellant loading system , 2018, Neurocomputing.

[23]  Richard D. Braatz,et al.  A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis , 2015, Comput. Chem. Eng..

[24]  P. Miller,et al.  Contribution plots: a missing link in multivariate quality control , 1998 .

[25]  Yi Cao,et al.  Kernel Canonical Variate Analysis for Nonlinear Dynamic Process Monitoring , 2015 .

[26]  Jie Zhang,et al.  Actuator fault monitoring and fault tolerant control in distillation columns , 2015, ICAC.

[27]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[28]  Leo H. Chiang,et al.  Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis , 2000 .

[29]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .