A New Multi-Objective Decision-Making Approach Applied to the Tennessee Eastman Process

Abstract In this paper, a generic framework and a new methodology aiming to decisions fusion of various Fault Detection and Diagnosis (FDD) methods are proposed. The framework consists of a discrete Bayesian Network (BN) and can handle all FDD methods, regardless of their a prior knowledge or requirements. The methodology expresses the FDD objectives to achieve the desired performance and results in a theoretical learning of the BN parameters. The development leads to a multi-objective problem under constraints, resolved with a lexicographic method. The effectiveness of the proposed Multi-Objective Decision-Making (MODM) approach is validated through the Tennessee Eastman Process (TEP), as a challenging industrial benchmark problem. The application shows the significant improvement in FDD performances that can be ensured by the proposed methodology, in terms of high fault detection rate and small false alarm rate.

[1]  Yang Zhao,et al.  Diagnostic Bayesian networks for diagnosing air handling units faults, Part II::Faults in coils and sensors , 2015 .

[2]  Jing Li,et al.  Fault detection and isolation of faults in a multivariate process with Bayesian network , 2010 .

[3]  Xiuxi Li,et al.  Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis , 2000 .

[4]  Shengwei Wang,et al.  An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network , 2013 .

[5]  Sylvain Verron,et al.  Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges , 2016, Annu. Rev. Control..

[6]  Petr Ekel,et al.  A new fault classification approach applied to Tennessee Eastman benchmark process , 2016, Appl. Soft Comput..

[7]  Nick Cercone,et al.  Bayesian network modeling for evolutionary genetic structures , 2010, Comput. Math. Appl..

[8]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[9]  N. Arunkumar,et al.  Linear approach for solving a piecewise linear vendor selection problem of quantity discounts using lexicographic method , 2006 .

[10]  Xin Gao,et al.  An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process , 2016, Neurocomputing.

[11]  Steven X. Ding,et al.  Improved canonical correlation analysis-based fault detection methods for industrial processes , 2016 .

[12]  Yew Seng Ng,et al.  Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods , 2011, Comput. Chem. Eng..

[13]  Bart De Moor,et al.  Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection , 2003, Artif. Intell. Medicine.

[14]  Abdessamad Kobi,et al.  Fault Detection and Diagnosis in a Bayesian Network classifier incorporating probabilistic boundary1 , 2015 .

[15]  Ping Zhang,et al.  Subspace method aided data-driven design of fault detection and isolation systems , 2009 .

[16]  Abdessamad Kobi,et al.  Fault detection and identification with a new feature selection based on mutual information , 2008 .

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

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

[19]  Abdessamad Kobi,et al.  Fault diagnosis of industrial systems by conditional Gaussian network including a distance rejection criterion , 2010, Eng. Appl. Artif. Intell..

[20]  Arthur K. Kordon,et al.  Fault diagnosis based on Fisher discriminant analysis and support vector machines , 2004, Comput. Chem. Eng..

[21]  Zhiqiang Ge,et al.  Decision fusion systems for fault detection and identification in industrial processes , 2015 .

[22]  S. Gaymard,et al.  Conditional respect towards the pedestrian: difference between men and women and risk modeling by the Bayesian approach , 2014 .

[23]  Fuad Rahman,et al.  Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variations , 2002, Document Analysis Systems.

[24]  Soumaya Yacout,et al.  Fault detection and diagnosis in the Tennessee Eastman Process using interpretable knowledge discovery , 2017, 2017 Annual Reliability and Maintainability Symposium (RAMS).