Research Article | OPEN ACCESS
A Comparative Study about the Effectiveness of Observers and Bayesian Belief Networks for the Fault Detection and Isolation in Power Electronics
1, 2Abbass Zein Eddine, 1Iyad Zaarour, 2Francois Guerin, 1Abbas Hijazi and 2Dimitri Lefebvre
1Lebanese University, Hadat, Beiruth 1003, Lebanon
2GREAH-Université Le Havre, 25 rue Philippe Lebon, 76058 Le Havre Cedex, France
Research Journal of Applied Sciences, Engineering and Technology 2017 1:10-28
Received: May ‎13, ‎2016 | Accepted: July ‎18, ‎2016 | Published: January 15, 2017
Abstract
The aim of this study is to highlight the capabilities of Bayesian Belief Network (BBN) in the domain of Fault Detection and Isolation (FDI) in DC/DC converter. Reliable electrical supplying systems are those which can provide continuously electrical energy to the consumers. This continuity requires fault free processes during all the phases of energy production, transfer and conversion. In order to achieve a fault free process it is mandatory to have an FDI system that holds on the faulty cases. In this study a Bayesian Naive Classifier (BNC) structure was selected and used as a first attempt to use BBNs for DC/DC power converter FDI.
Keywords:
Bayesian na, DC/DC power converter, fault detection and isolation, proportional observer,
References
-
Friedman, N., D. Geiger and M. Goldszmidt, 1997. Bayesian network classifiers. Mach. Learn., 29(2-3): 131-163.
CrossRef
-
Bartram, G. and S. Mahadevan, 2013. Dynamic bayesian networks for prognosis. Proceeding of the Annual Conference of the Prognostics and Health Management Society.
Direct Link
- Berendsen, C.S., G. Champenois, J. Davoine and G. Rostaing, 1992. How to detect and to localize a fault in a DC/DC converter? Proceeding of the International Conference on Industrial Electronics, Control, Instrumentation and Automation and Power Electronics and Motion Control. San Diego, CA, USA, pp: 536-541.
CrossRef
- Colot, O., C. Olivier, P. Courtellemont and A. El Matouat, 1994. Information Criteria and Abrupt Changes in Probability Laws. In: Signal Processing VII: Theory and Applications, pp: 1855-1858.
-
Cowell, R., 1998. Introduction to Inference for Bayesian Networks. In: Jordan, M.I. (Ed.), Learning in Graphical Models. Springer, Netherlands, pp: 9-26.
CrossRef
-
Flores, M.J., A.E. Nicholson, A. Brunskill, K.B. Korb and S. Mascaro, 2011. Incorporating expert knowledge when learning Bayesian network structure: A medical case study. Artif. Intell. Med., 53(3): 181-204.
CrossRef PMid:21958683
-
Gao, J., Y. Ji, J. Bals and R. Kennel, 2012. Fault detection of power electronic circuit using wavelet analysis in modelica. Proceeding of the 9th International MODELICA Conference. Munich, Germany, pp: 513-522.
CrossRef
- Grossman, D. and P. Domingos, 2004. Learning bayesian network classifiers by maximizing conditional likelihood. Proceeding of the 21st International Conference on Machine Learning. Banff, Canada, pp: 46.
CrossRef
- Guerin, F. and D. Lefebvre, 2009. Residual analysis for the diagnosis of hybrid electrical energy systems. IFAC P. Ser., 42(8): 1366-1371.
CrossRef
-
Guerin, F. and D. Lefebvre, 2013. Adaptive generalized PID controllers and fuzzy logic coordinator for load sharing in SMSE. Proceeding of the IEEE 52nd Annual Conference on Decision and Control (CDC, 2013). Firenze, Italy, pp: 5588-5593.
CrossRef
- Guerin, F., C. Labarre and D. Lefebvre, 2011a. Magnetic near-field measurement for FDI of ZVS full bridge isolated buck converter. Proceeding of the IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED, 2011). Bologna, Italy, pp: 344-349.
CrossRef
-
Guérin, F., D. Lefebvre, A.B. Mboup, J.Y. Parédé, E. Lemains and P.A.S. Ndiaye, 2011b. Hybrid modeling for performance evaluation of multisource renewable energy systems. IEEE T. Autom. Sci. Eng., 8(3): 570-580.
CrossRef
- Guerin, F., D. Lefebvre and V. Loisel, 2012. Supervisory control design for systems of multiple sources of energy. Control Eng. Pract., 20(12): 1310-1324.
CrossRef
- Guo, H. and W. Hsu, 2002. A survey of algorithms for real-time Bayesian network inference. Proceeding of the AAAI/KDD/UAI02 Joint Workshop on Real-Time Decision Support and Diagnosis Systems. Edmonton, Canada.
- Huang, C. and A. Darwiche, 1994. Inference in belief networks: A procedural guide. Int. J. Approx. Reason., 11(1): 158.
Direct Link
-
Jensen, F.V., 1996. An Introduction to Bayesian Networks. Vol. 210, UCL London.
-
Kamel, T., Y. Biletskiy, C.P. Diduh and L. Chang, 2015. Open circuit fault diagnoses for power electronic converters. Proceeding of the IEEE 24th International Symposium on Industrial Electronics (ISIE). Buzios, pp: 361-366.
CrossRef
- Khanafer, R.M., B. Solana, J. Triola, R. Barco, L. Moltsen, Z. Altman and P. Lazaro, 2008. Automated diagnosis for UMTS networks using Bayesian network approach. IEEE T. Veh. Technol., 57(4): 2451-2461.
CrossRef
- Kim, S.Y., K. Nam, H.S. Song and H.G. Kim, 2008. Fault diagnosis of a ZVS DC–DC converter based on DC-link current pulse shapes. IEEE T. Ind. Electron., 55(3): 1491-1494.
CrossRef
- Langley, P., W. Iba and K. Thompson, 1992. An analysis of Bayesian classifiers. Proceeding of the 10th National Conference on Artificial Intelligence (AAAI, 1992), pp: 223-228.
Direct Link
- Levin, K.T., E.M. Hope and A.D. Domínguez-Garcíay, 2010. Observer-based fault diagnosis of power electronics systems. Proceeding of the IEEE Energy Conversion Congress and Exposition (ECCE). Atlanta, GA, USA, pp: 4434-4440.
CrossRef
- Madden, M.G., 2003. The performance of Bayesian network classifiers constructed using different techniques. Proceeding of the European Conference on Machine Learning, Workshop on Probabilistic Graphical Models for Classification, pp: 59-70.
PMid:12611115
- Mboup, A.B., F. Guérin, P.A. Ndiaye and D. Lefebvre, 2008. Multimodel for the coupling of several dc/dc power converters on a dc bus. Proceeding of the IEEE International Symposium on Industrial Electronics (ISIE,2008). Cambridge, England, pp: 1507-1512.
-
Meziane, H., C. Labarre, S. Lefteriu, M. Defoort and M. Djmemai, 2015. Fault detection and isolation for a multi-cellular converter based on sliding mode observer. IFAC-PapersOnLine, 48(21): 164-170.
CrossRef
- Pearl, J., 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Kaufmann, San Mateo, California.
Direct Link
- Pearl, J., 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge.
- Redner, R.A. and H.F. Walker, 1984. Mixture densities, maximum likelihood and the EM algorithm. SIAM Rev., 26(2): 195-239.
CrossRef
- Saad, A., I. Zaarour, A. Zeinedine, M. Ayache, P. Bejjani, F. Guerin and D. Lefebvre, 2013. A preliminary study of the causality of freezing of gait for parkinson's disease patients: Bayesian belief network approach. IJCSI Int. J. Comput. Sci. Issues, 10(3): 88-95.
Direct Link
-
Tiplica, T., S. Verron, A. Kobi and I. Nastac, 2006. FDI in multivariate process with naive bayesian network in the space of discriminant factors. Proceeding of the International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and International Commerce (CIMCA'06). Sydney, NSW, pp: 216-216.
CrossRef
- Volosencu, C. and I.D. Curiac, 2010. Fault detection and diagnosis of distributed parameter systems based on sensor networks and Bayesian networks. Proceeding of the 6th WSEAS International Conference on Dynamical Systems and Control, pp: 167-173.
Direct Link
- Wiegerinck, W., 2005. Modeling Bayesian Networks by Learning from Experts. Proceeding of the BNAIC, pp: 305-312.
Direct Link
- Zaarour, I., A. Saad, A.Z. Eddine, M. Ayache, F. Guerin, P. Bejjani and D. Lefebvre, 2015. Methodologies for the Diagnosis of the Main Behavioral Syndromes for Parkinson's Disease with Bayesian Belief Networks. In: Tran, Q.N. and H. Arabnia (Eds.), Emerging Trends in Computational Biology, Bioinformatics and Systems Biology. Morgan Kaufmann, Boston, pp: 467-485.
CrossRef
-
Zein Eddine, A.H., I. Zaarour, F. Guerin, A. Hijazi and D. Lefebvre, 2015. Fault detection and isolation for ZVS full bridge isolated buck converter based on: Observer design and Bayesian network. Int. J. Adv. Res. Comput. Commun. Eng., 4(7): 242-249.
Direct Link
-
Zein Eddine, A., I. Zaarour, F. Guerin, A. Hijazi and D. Lefebvre, 2016. Improving fault isolation in DC/DC converters based with bayesian belief networks. Proceeding of the 4th IFAC International Conference on Intelligent Control and Automation Science (ICONS), France.
Direct Link
- Zheng, F. and G.I. Webb, 2005. A comparative study of semi-naive bayes methods in classification learning. Proceeding of the 4th Australasian Data Mining Conference (AusDM05). Sydney, pp: 141-156.
PMCid:PMC1361771
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|