Diagnosis of faults in plant operations and scheduling under uncertain production times

................................................................................................................................... iii Acknowledgments.................................................................................................................... iv Table of

[1]  Jie Yu,et al.  Maximized mutual information based non-Gaussian subspace projection method for quality relevant process monitoring and fault detection , 2013, 52nd IEEE Conference on Decision and Control.

[2]  Nir Friedman,et al.  Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks , 2004, Machine Learning.

[3]  Daphne Koller,et al.  Nonuniform Dynamic Discretization in Hybrid Networks , 1997, UAI.

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

[5]  Bart De Moor,et al.  Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks , 2006, ISMB.

[6]  J. Macgregor,et al.  Development of inferential process models using PLS , 1994 .

[7]  Harald Steck.  On the Use of Skeletons when Learning in Bayesian Networks , 2000, UAI.

[8]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[9]  Karlene A. Kosanovich,et al.  Improved Process Understanding Using Multiway Principal Component Analysis , 1996 .

[10]  Changhe Yuan,et al.  Hybrid Loopy Belief Propagation , 2006, Probabilistic Graphical Models.

[11]  Jie Yu,et al.  Quality relevant nonlinear batch process performance monitoring using a kernel based multiway non-Gaussian latent subspace projection approach , 2014 .

[12]  Mirko Perkusich,et al.  A procedure to detect problems of processes in software development projects using Bayesian networks , 2015, Expert Syst. Appl..

[13]  Leo H. Chiang,et al.  Process monitoring using causal map and multivariate statistics: fault detection and identification , 2003 .

[14]  Raghunathan Rengaswamy,et al.  Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets , 2004, Eng. Appl. Artif. Intell..

[15]  Craig Boutilier,et al.  Context-Specific Independence in Bayesian Networks , 1996, UAI.

[16]  Sanjeev S. Tambe,et al.  Soft-sensor development for fed-batch bioreactors using support vector regression , 2006 .

[17]  Marie desJardins,et al.  LEARNING STRUCTURED BAYESIAN NETWORKS: COMBINING ABSTRACTION HIERARCHIES AND TREE‐STRUCTURED CONDITIONAL PROBABILITY TABLES , 2008, Comput. Intell..

[18]  Xiao Fan Wang,et al.  Soft sensing modeling based on support vector machine and Bayesian model selection , 2004, Comput. Chem. Eng..

[19]  David Maxwell Chickering,et al.  A Bayesian Approach to Learning Bayesian Networks with Local Structure , 1997, UAI.

[20]  Raghunathan Rengaswamy,et al.  A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis , 2007 .

[21]  Dale E. Seborg,et al.  Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis , 2005 .

[22]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[23]  Rafael Rumí,et al.  Approximate probability propagation with mixtures of truncated exponentials , 2007, Int. J. Approx. Reason..

[24]  N. Wermuth,et al.  Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative , 1989 .

[25]  Nir Friedman,et al.  Learning Bayesian Networks with Local Structure , 1996, UAI.

[26]  Bernard Yannou,et al.  Identifying product failure rate based on a conditional Bayesian network classifier , 2011, Expert Syst. Appl..

[27]  Heikki N. Koivo,et al.  Application of artificial neural networks in process fault diagnosis , 1991, Autom..

[28]  Francis J. Doyle,et al.  Nonlinear inferential control for process applications , 1997 .

[29]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[30]  Luis Puigjaner,et al.  Performance assessment of a novel fault diagnosis system based on support vector machines , 2009, Comput. Chem. Eng..

[31]  Jean-Marie Flaus,et al.  COMPARISON OF ESTIMATION METHODS FOR BIOTECHNOLOGICAL PROCESSES , 1988 .

[32]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[33]  S. Qin,et al.  Root cause diagnosis of plant-wide oscillations using Granger causality , 2014 .

[34]  Venkat Venkatasubramanian,et al.  PCA-SDG based process monitoring and fault diagnosis , 1999 .

[35]  Adnan Darwiche,et al.  Compiling Bayesian Networks Using Variable Elimination , 2007, IJCAI.

[36]  Daniel Nikovski,et al.  Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics , 2000, IEEE Trans. Knowl. Data Eng..

[37]  Serafín Moral,et al.  Mixtures of Truncated Exponentials in Hybrid Bayesian Networks , 2001, ECSQARU.

[38]  David Poole,et al.  Efficient Inference in Large Discrete Domains , 2002, UAI.

[39]  Jie Yu,et al.  A quality relevant non‐Gaussian latent subspace projection method for chemical process monitoring and fault detection , 2014 .

[40]  S. Skogestad,et al.  Estimation of distillation compositions from multiple temperature measurements using partial-least-squares regression , 1991 .

[41]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[42]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[43]  Daphne Koller,et al.  Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks , 2005, UAI.

[44]  Jie Yu.  A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes , 2012 .

[45]  Nevin Lianwen Zhang,et al.  Exploiting Contextual Independence In Probabilistic Inference , 2011, J. Artif. Intell. Res..

[46]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[47]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[48]  Barry Lennox,et al.  Integrated condition monitoring and control of fed-batch fermentation processes , 2004 .

[49]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[50]  K.A. Hoo,et al.  Multivariate statistics for process control , 2002, IEEE Control Systems.

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

[52]  Jie Yu,et al.  A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses , 2012, Comput. Chem. Eng..

[53]  Rafael Rumí,et al.  Bayesian Network Models with Discrete and Continuous Variables , 2007 .

[54]  Steffen L. Lauritzen,et al.  Stable local computation with conditional Gaussian distributions , 2001, Stat. Comput..

[55]  Dragomir Anguelov,et al.  A General Algorithm for Approximate Inference and Its Application to Hybrid Bayes Nets , 1999, UAI.

[56]  Pedro M. Domingos,et al.  Structured Message Passing , 2013, UAI.

[57]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[58]  Anders L. Madsen,et al.  Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes , 2005, Comput. Chem. Eng..

[59]  S. Lauritzen.  Propagation of Probabilities, Means, and Variances in Mixed Graphical Association Models , 1992 .

[60]  Sten Bay Jørgensen,et al.  A systematic approach for soft sensor development , 2007, Comput. Chem. Eng..

[61]  Joanne Bechta Dugan,et al.  A discrete-time Bayesian network reliability modeling and analysis framework , 2005, Reliab. Eng. Syst. Saf..

[62]  Jie Yu,et al.  A novel dynamic bayesian network‐based networked process monitoring approach for fault detection, propagation identification, and root cause diagnosis , 2013 .

[63]  Gülçin Büyüközkan,et al.  Assessment of lean manufacturing effect on business performance using Bayesian Belief Networks , 2015, Expert Syst. Appl..

[64]  Hiromasa Kaneko,et al.  Development of a new soft sensor method using independent component analysis and partial least squares , 2009 .

[65]  Andrés Cano,et al.  Extended Probability Trees for Probabilistic Graphical Models , 2014, Probabilistic Graphical Models.

[66]  ChangKyoo Yoo,et al.  Statistical process monitoring with independent component analysis , 2004 .

[67]  Ana Cristina Vieira de Melo,et al.  Software maintenance project delays prediction using Bayesian Networks , 2008, Expert Syst. Appl..

[68]  Nina F. Thornhill,et al.  A practical method for identifying the propagation path of plant-wide disturbances , 2008 .

[69]  Biao Huang.  Bayesian methods for control loop monitoring and diagnosis , 2008 .

[70]  Ping Li,et al.  Soft Sensor Modeling Based on DICA-SVR , 2005, ICIC.