Abstract
Fault diagnosis in complex systems is important due to the impact it may have for reducing breakage costs or for avoiding production losses in industrial systems. Several approaches have been proposed for fault diagnosis, some of which are based on Bayesian Networks. Bayesian Networks are an adequate formalism for representing and reasoning under uncertainty conditions, however, they do not scale well for complex systems. For overcoming this limitation, researchers have proposed Multiply Sectioned Bayesian Networks. These are an extension of the Bayesian Networks for representing large domains, while ensuring the network inference in an efficient way. In this work we propose a distributed method for fault diagnosis in complex systems using Multiply Sectioned Bayesian Networks. The method was tested in the detection of multiple faults in combinational logic circuits showing comparable results with the literature in terms of accuracy, but with a significant reduction in the runtime.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bar-Yam, Y.: Dynamics of Complex Systems, vol. 213. Addison-Wesley, Reading (1997)
Bertrand-Krajewski, J.L., Winkler, S., Saracevic, E., Torres, A., Schaar, H.: Comparison of and uncertainties in raw sewage cod measurements by laboratory techniques and field UV-visible spectrometry. Water Sci. Technol. 56(11), 17–25 (2007)
Böhme, T., Cox, C., Valentin, N., Denoeux, T.: Comparison of autoassociative neural networks and kohonen maps for signal failure detection and reconstruction. Intell. Eng. Syst. Through Artif. Neural Netw. 9, 637–644 (1991)
Branisavljević, N., Kapelan, Z., Prodanović, D.: Improved real-time data anomaly detection using context classification. J. Hydroinformatics 13(3), 307–323 (2011)
Chowdhury, G.G.: Introduction to Modern Information Retrieval. Facet Publishing, London (2010)
Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks. Artif. Intell. 42(2–3), 393–405 (1990)
Eryurek, E., Upadhyaya, B.: Sensor validation for power plants using adaptive backpropagation neural network. IEEE Trans. Nuclear Sci. 37(2), 1040–1047 (1990)
Goebel, K., Agogino, A.: An architecture for fuzzy sensor validation and fusion for vehicle following in automated highways. In: Proceedings of the 29th International Symposium on Automotive Technology and Automation (1996)
Guo, T.H., Nurre, J.: Sensor failure detection and recovery by neural networks. In: Seattle International Joint Conference on Neural Networks, IJCNN 1991, vol. 1, pp. 221–226. IEEE (1991)
Haykin, S.S., et al.: Kalman Filtering and Neural Networks. Wiley, Hoboken (2001)
Holbert, K.E., Heger, A.S., Alang-Rashid, N.K.: Redundant sensor validation by using fuzzy logic. Nuclear Sci. Eng. 118(1), 54–64 (1994)
Ibargüengoytia, P.H., Vadera, S., Sucar, L.E.: A probabilistic model for information and sensor validation. Comput. J. 49(1), 113–126 (2005)
Ibarguengoytia, P., et al.: Any time probabilistic sensor validation. Ph.D. thesis, University of Salford, UK (1997)
Khadem, M., Alexandro, F., Colley, R.: Sensor validation in power plants using neural networks. In: Neural Network Computing for the Electric Power Industry, pp. 51–54 (1993)
Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems. J. Roy. Stat. Soc. Ser. B (Methodol.) 50, 157–224 (1988)
Napolitano, M.R., Windon, D.A., Casanova, J.L., Innocenti, M., Silvestri, G.: Kalman filters and neural-network schemes for sensor validation in flight control systems. IEEE Trans. Control Syst. Technol. 6(5), 596–611 (1998)
Rajakarunakaran, S., Venkumar, P., Devaraj, D., Rao, K.S.P.: Artificial neural network approach for fault detection in rotary system. Appl. Soft Comput. 8(1), 740–748 (2008)
Samanta, B.: Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech. Syst. Sig. Process. 18(3), 625–644 (2004)
Sucar, L.E.: Probabilistic Graphical Models - Principles and Applications. Advances in Computer Vision and Pattern Recognition. Springer, Heidelberg (2015). https://doi.org/10.1007/978-1-4471-6699-3
Sun, S., et al.: Literature review for data validation methods. Sci. Technol. 47(2), 95–102 (2011)
Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1(Jun), 211–244 (2001)
Valentin, N., et al.: A neural network-based software sensor for coagulation control in a water treatment plant. Intell. Data Anal. 5(1), 23–39 (2001)
Xiang, Y.: Webweavr-iv research toolkit (2006)
Xiang, Y.: Comparison of multiagent inference methods in multiply sectioned Bayesian networks. Int. J. Approx. Reason. 33(3), 235–254 (2003)
Xiang, Y., Jensen, F.V., Chen, X.: Inference in multiply sectioned Bayesian networks: methods and performance comparison. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 36(3), 546–558 (2005)
Xiang, Y., Poole, D., Beddoes, M.P.: Multiply sectioned Bayesian networks and junction forests for large knowledge-based systems. Comput. Intell. 9(2), 171–220 (1993)
Acknowledgement
This work was sponsored by CEMIE-Eolico (CONACYT and SENER) and INAOE. The first author gratefully acknowledges CONACyT for her master scholarship 611489.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Oña García, A.L., Sucar, L.E., Morales, E.F. (2018). A Distributed Probabilistic Model for Fault Diagnosis. In: Simari, G., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-03928-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03927-1
Online ISBN: 978-3-030-03928-8
eBook Packages: Computer ScienceComputer Science (R0)