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Intelligent Hybrid Fault Diagnostic System

by A.H. Mohamed, M.H. El-Fouly
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 7
Year of Publication: 2015
Authors: A.H. Mohamed, M.H. El-Fouly
10.5120/ijais2015451438

A.H. Mohamed, M.H. El-Fouly . Intelligent Hybrid Fault Diagnostic System. International Journal of Applied Information Systems. 9, 7 ( September 2015), 17-21. DOI=10.5120/ijais2015451438

@article{ 10.5120/ijais2015451438,
author = { A.H. Mohamed, M.H. El-Fouly },
title = { Intelligent Hybrid Fault Diagnostic System },
journal = { International Journal of Applied Information Systems },
issue_date = { September 2015 },
volume = { 9 },
number = { 7 },
month = { September },
year = { 2015 },
issn = { 2249-0868 },
pages = { 17-21 },
numpages = {9},
url = { https://www.ijais.org/archives/volume9/number7/819-2015451438/ },
doi = { 10.5120/ijais2015451438 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:00:32.810347+05:30
%A A.H. Mohamed
%A M.H. El-Fouly
%T Intelligent Hybrid Fault Diagnostic System
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 9
%N 7
%P 17-21
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently, intelligent hybrid fault diagnostic systems are widely used to deal with the modern complex systems. It is found that, case based reasoning approach has proved its powerful as modern diagnostic technique. It can simplify the computation analysis required for diagnosis the complex systems. But, they still suffer from some limitations due to: (1) its inability to achieve a diagnosis for new faults that having no similar cases in its case library. (2) problems of storing the cases in the library and its effect on the complexity of the retrieving and adaptation processes. The proposed research introduces a new intelligent hybrid system that integrates the case based reasoning, neural network, and the genetic algorithm approaches to improve the performance of the CBR diagnostic systems. It incorporates the neural network into the case based system to diagnose the new faults. While, it uses the genetic algorithm to optimize the topology of the neural network and to select the optimum cases to be stored in the case library to manage its size. Therefore, the proposed hybrid diagnostic system can increase the accuracy, decrease the time and simplify the complexity of the retrieving and adaptation processes of CBR systems. The suggested system has applied for diagnosis the faults of a Wireless Network. This network communicates between a robot arm used for cleaning the solar cell panels of the complex PV systems, and its control centre as a case of study. The obtained results have proved good performance of the proposed hybrid diagnostic system in the practical sites.

References
  1. Teodorovic, D., Šelmic, M., and Teodorovic, L. M. 2013. "Combining Case-Based Reasoning with Bee Colony Optimization for Dose Planning in Well Differentiated Thyroid Cancer Treatment", Expert Systems with Applications, 40: 2147–2155.
  2. Beddoe, G., Petrovic, S. , and Li, J. 2009. "A Hybrid Meta-Heuristic Case-Based Reasoning System for Nurse Roistering", Journal of Scheduling 12 (2): 99-119.
  3. Luger, F. G. and Stubblefield, A. 2011. Addison Wesley Longman Inc., Third Editions, USA, pp.125-140.
  4. Oman, S. and Cunningham, P. 2001. "Using Case Retrieval to Seed Genetic Algorithms", International Journal of Computational Intelligence and Applications, 1(1): 71-82.
  5. Burke, E. , MacCarthy, B., Petrovic, S. and Qu, R. 2006. "Multiple-Retrieval Case-Based Reasoning for Course Timetabling Problems", Journal of Operations Research Society, 57 (2):148-162.
  6. Aamodt A. and Plaza, E. 1994. "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches", Artificial Intelligence Communications, 7:39-52.
  7. Madureira, A., Santos, J. and Pereira, I. 2009. A Hybrid Intelligent System for Distributed Dynamic Scheduling, Natural Intelligence for Scheduling, Planning and Packing Problems, Studies in Computational Intelligence, eds. R.Chiong, and S.Dhakal, 250: 295-324.
  8. Lee, C.-H. L. , Liu, A. and Huang, H.-H. 2010. "Using Planning and Case-Based Reasoning for Service Composition", Journal of Advanced Computational Intelligence and Intelligent Informatics, 14 (5):450-456.
  9. Kwang H. I. and Sang, C. P. 2007. "Case-Based Reasoning and Neural Network Based Expert System for Personalization", Expert Systems with Applications 32:77–85.
  10. Chen D. and Burrell, P. 2001. "Case-Based Reasoning System and Artificial Neural Networks: A Review", Neural Computation and Application, Springer-Verlag London Limited, 10:264–273.
  11. Musa, A. G., Daramola, O., Owoloko, A., Olugbara, O. 2013. "A Neural-CBR System for Real Property Valuation", Journal of Emerging Trends in Computing and Information Sciences, 4(8):611-622.
  12. Mala, D. J., Elizabeth S. R. and Mohan, V. 2008. "Intelligent Test Case Optimizer - An Automated Hybrid Genetic Algorithm based Test Case Optimization Framework", International Journal of Computer Science and Applications, 1(1):51-55.
  13. Sànchez-Marrè1, M., Gibert, K. , Vinayagam, R. K. , S.-Villanueva, B. 2014. "Evolutionary Computation and Case-Based Reasoning Interoperation in IEDSS through GESCONDA, International Environmental Modeling and Software Society (iEMSs), 7th Intl. Congress on Env. Modeling and Software, San Diego, CA, USA, Daniel P. Ames, Nigel W.T. Quinn and Andrea E. Rizzoli (Eds.).
  14. Dou, J. , Chang, K.-T., Chen, S., Yunus, A. P., Liu, J.-K., Xia H. and Zhu, Z. 2015. "Automatic Case- Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm, Remote Sensing, 7:4318-4342.
  15. Ahn, H., Kim, K. , and Han, I. 2006. "Hybrid Genetic Algorithms and Case-Based Reasoning Systems for Customer Classification", Expert System, 23:127–144.
  16. Minor, M., Bergmann, R., and Görg, S. 2014. "Case-based Adaptation of Workflows", Information Systems, 40: 142–152.
  17. Cordier, A., Mascret, B. and Mille, A. 2013. “Dynamic Case Based Reasoning for Contextual Reuse of Experience”, Case-Based Reasoning Workshop, Cindy Marling ed. Alessandria, Italy, 69-78.
  18. Prentzas, J. and Harzilygeroudis, I. 2009. "Combination of Case-Based Reasoning with other Intelligent Methods", International Journal of Hybrid Intelligent systems, 6(2809): 189-209.
  19. Plaza, E. and Mcginty, L. 2013. “Distributed Case-Based Reasoning, The Knowledge Engineering” Review, Cambridge University Press,72(4):30-37.
  20. Hidayah, I. , Syahrina, A. and Permanasari, A. E. 2012. "Student Modeling using Case-Based Reasoning in Conventional Learning System", (IJCSIS) International Journal of Computer Science and Information Security, 10(10):21-27.
  21. Azuaje, F., Dubitzky, W., Lopes, P., Black, N. and Adamsom, K. 2007. "A Neural Network Approach", Artificial Intelligence in Medicine, 15: 275–297.
  22. Gong, L. , Liu, C., Li, Y. and Yuan, F. 2012. "Training Feed-forward Neural Networks Using the Gradient Descent Method with the Optimal Stepsize", Journal of Computational Information Systems 8(4):1359-1371.
  23. HUK, M. 2012. "Back-Propagation Generalized Delta Rule For The Selective Attention Sigma–If Artificial Neural Network", International Journal of Applied Mathematics and Computer Science, 22 (2):449–459.
  24. Zhao, Q. and Ye, F. 2013. "A New Back-Propagation Neural Network Algorithm for a Big Data Environment Based on Punishing Characterized Active Learning Strategy", International Journal of Knowledge and Systems Science, 4(4): 32-45.
  25. Brill, F.Z., Brown, D.E., Martin, W.N. 2012. “Fast Genetic Selection of Features for Neural Network Classifiers”, IEEE Transactions on Neural Networks, 3:324-328.
  26. Yang, J. and Honavar, V. 2012. “Feature Subset Selection Using a Genetic Algorithm”, IEEE Intelligent Systems, 13:44-49.
  27. Gharehchopogh, F. S., Molany M. and Mokri, F. D. 2013. "Using Artificial Neural Network in Diagnosis of Thyroid Disease: A Case Study", International Journal on Computational Sciences and Applications (IJCSA), 3(4):49-61.
Index Terms

Computer Science
Information Sciences

Keywords

Case-based reasoning genetic algorithm neural network fault diagnosis and PV System.

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