An approach for developing diagnostic Bayesian network based on operation procedures

A method to develop Bayesian network based on operation procedures is proposed.The diagnostic model consists of faults, symptoms and operation procedures.The presented approach is applied to a case study of closing BOP. In this paper, a novel approach of developing the Bayesian network for fault diagnosis based on operation procedures is presented. The proposed Bayesian network consists of operation procedure layer, fault layer and fault symptom layer. First, operation procedure layer containing procedure nodes and state decision nodes is developed. Second, the fault layer is determined based on the state decision nodes in the operation procedure layer. Then fault symptom layer including symptoms sensitive to the concerned faults is developed. Finally, the entire Bayesian network is established by integrating the three layers. The presented approach is applied to hydraulic control system of subsea blowout preventer (BOP). Taking an example of closing the BOP, the operation procedures are illustrated. The entire Bayesian network for fault diagnosis of closing the BOP is established. Several cases possible to appear during the closing process are studied to evaluate the developed model.

[1]  Nicandro Cruz-Ramírez,et al.  Diagnosis of breast cancer using Bayesian networks: A case study , 2007, Comput. Biol. Medicine.

[2]  Marcelo Godoy Simões,et al.  On-line fault diagnostic system for proton exchange membrane fuel cells , 2008 .

[3]  Ahmad B. Rad,et al.  Bond graph based Bayesian network for fault diagnosis , 2011, Appl. Soft Comput..

[4]  Sheau-Dong Lang,et al.  Adapting a diagnostic problem-solving model to information retrieval , 2000, Inf. Process. Manag..

[5]  Norman E. Fenton,et al.  Improved reliability modeling using Bayesian networks and dynamic discretization , 2010, Reliab. Eng. Syst. Saf..

[6]  Milan Studený,et al.  Learning Bayesian network structure: Towards the essential graph by integer linear programming tools , 2014, Int. J. Approx. Reason..

[7]  Ioan Dumitrache,et al.  Medicine expert system dynamic Bayesian Network and ontology based , 2011, Expert Syst. Appl..

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

[9]  Emilie Philippot,et al.  Bayesian networks for incomplete data analysis in form processing , 2014, International Journal of Machine Learning and Cybernetics.

[10]  Ziya Arnavut,et al.  Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization , 2007, Parallel Comput..

[11]  Franck Guarnieri,et al.  A Bayesian network to manage risks of maritime piracy against offshore oil fields , 2014 .

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

[13]  Andrés R. Masegosa,et al.  New skeleton-based approaches for Bayesian structure learning of Bayesian networks , 2013, Appl. Soft Comput..

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

[15]  Edwin Villanueva,et al.  Efficient methods for learning Bayesian network super-structures , 2014, Neurocomputing.

[16]  Shuxue Ding,et al.  Diagnose the mild cognitive impairment by constructing Bayesian network with missing data , 2011, Expert Syst. Appl..

[17]  Mattias Nyberg,et al.  Modeling and inference for troubleshooting with interventions applied to a heavy truck auxiliary braking system , 2012, Eng. Appl. Artif. Intell..

[18]  Peter J. F. Lucas,et al.  Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare , 2014, Int. J. Approx. Reason..

[19]  Luigi Portinale,et al.  Improving the analysis of dependable systems by mapping fault trees into Bayesian networks , 2001, Reliab. Eng. Syst. Saf..

[20]  Fu Xiao,et al.  Bayesian network based FDD strategy for variable air volume terminals , 2014 .

[21]  Sagar Patel,et al.  Knowledge acquisition for diagnosis model in wireless networks , 2009, Expert Syst. Appl..

[22]  Adel Alaeddini,et al.  Using Bayesian networks for root cause analysis in statistical process control , 2011, Expert Syst. Appl..

[23]  Haytham Elghazel,et al.  A hybrid algorithm for Bayesian network structure learning with application to multi-label learning , 2014, Expert Syst. Appl..