Development and application of a decision making tool for fault diagnosis of turbocompressor based on Bayesian network and fault tree

• Autorzy: Lakehal Abdelaziz , Nahal Mourad , Harouz Riad

Identyfikatory

DOI

10.24425/mper.2019.129565

• Języki publikacji: EN

Abstrakty

EN

Fault Tree is one of the traditional and conventional approaches used in fault diagnosis. By identifying combinations of faults in a logical framework it’s possible to define the structure of the fault tree. The same go with Bayesian networks, but the difference of these probabilistic tools is in their ability to reasoning under uncertainty. Some typical constraints to the fault diagnosis have been eliminated by the conversion to a Bayesian network. This paper shows that information processing has become simple and easy through the use of Bayesian networks. The study presented showed that updating knowledge and exploiting new knowledge does not complicate calculations. The contribution is the structural approach of faults diagnosis of turbo compressor qualitatively and quantitatively, the most likely faults are defined in descending order. The approach presented in this paper has been successfully applied to turbo compressor, which represent vital equipment in petrochemical plant.

Słowa kluczowe

EN

plant maintenance; prioritization; Bayesian networks; fault tree; diagnosis; turbocompressor

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2019
• Tom: Vol. 10, No. 2
• Strony: 16–--24
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Lakehal Abdelaziz

• Department of Mechanical Engineering, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk-Ahras, 41000, Algeria

autor

Nahal Mourad

• Department of Mechanical Engineering, Mohamed Chérif Messaadia University, Algeria

autor

Harouz Riad

• Department of Mechanical Engineering, Mohamed Chérif Messaadia University, Algeria

Bibliografia

• [1] Chiang L.H., Russell E.L., Braatz R.D., Fault detection and diagnosis in industrial systems, New York: Springer-Verlag, 2001.
• [2] Sylvain V., Diagnostic et surveillance des processus complexes par réseaux bayésiens, Thèse de Doctorat Université d’Angers, 2007.
• [3] Hasan A.N., Mahdi A.S., Silvio S., Hamed D.B., Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques, Neurocomputing, 91, 29–47, 2012.
• [4] Weiguo Z., Kui L., Shaopu Y., Liying W., RVMbased adaboost scheme for stator interturn faults of the induction motor, Engineering Review, 36, 123– 131, 2016.
• [5] Landy G., AMDEC guide pratique, AFNOR, 2007.
• [6] Bobbio A., Portinale L., Minichino M., Ciancamerla E., Improving the analysis of dependable systems by mapping fault trees into Bayesian networks, Reliab Eng. Syst. Safety, 71, 249–260, 2001.
• [7] Lakehal A., Tachi F., Bayesian Duval Triangle Method for Fault Prediction and Assessment of Oil Immersed Transformers, Measurement and Control, 50, 4, 103–109, 2017.
• [8] Ferdous R., Khan F.I., Veitch B., Amyotte P., Methodology for computer aided fuzzy FT analysis, Journal of Process safety and Environmental Protection, 87, 217–226, 2009.
• [9] Lin C-T., Wang M-JJ., Hybrid FT analysis using fuzzy sets, Journal of Reliability Engineering and System Safety, 58, 205–13, 1997.
• [10] Weber P., Medina-Oliva G., Simon C., Iung B., Overview on Bayesian networks Applications for Dependability, Risk Analysis and Maintenance areas, Engineering Applications of Artificial Intelligence, 25, 4, 671–682, 2012.
• [11] Delcroix V., Maalej M.A., Piechowiak S., Bayesian networks versus other probabilistic models for the multiple diagnosis of large devices, Int. J. Artif. Intell. Tools, 16, 3, 417–433, 2007.
• [12] Lakehal A., Ghemari Z., Optimisation of an emergency plan in gas distribution network operations with Bayesian networks, International Journal of Reliability and Safety, 10, 3, 227–242, 2016.
• [13] Darwiche A., Modeling and Reasoning with Bayesian Networks, Cambridge University Press, 2009.
• [14] Naim P., Wuillemin P.H., Leray P., Pourret O., Becker A., Réseaux bayésiens – 2 ème édition, Eyrolles, France 2004.
• [15] Sou-Sen Leu, Ching-Miao Chang, Bayesian-network-based safety risk assessment for steel construction projects, Accident Analysis and Prevention, 54, 122–133, 2013.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).