Predicting Fault In Telecommunication Network: Lessons Learned
Citation
MLA Style: Mohd Izhan Mohd Yusoff. Predicting Fault In Telecommunication Network: Lessons Learned International Journal of Engineering Trends and Technology 68.12(2020):171-181.
APA Style:Mohd Izhan Mohd Yusoff. Predicting Fault In Telecommunication Network: Lessons Learned International Journal of Engineering Trends and Technology, 68(12), 171-181.
Abstract
Faults in the telecommunication network has attracted a large amount of interest from researchers and practitioners who have introduced, for example, algorithms to extract faulty signatures from noisy historical event data; rules and decision tree data mining classifiers to upgrade fault detection and handling, and cluster head selection algorithms to address the failure under uncertain situations. This article discusses lessons learned from studying and applying statistical methods or techniques to predict faults in the telecommunication networks (which is our main objective or target). Cochrane Orcutt`s approach or procedure was identified for having properties that we believe would fulfill or achieve the above objective or target. The challenges we faced were: real data collected from the device show seasonal trends (meaning the device is faulty periodically), and we showed, by using simulated seasonal data, the Cochrane Orcutt approach or procedure failed to get the desired results. We proposed the Cochrane Orcutt adjusted approach or procedure where we showed, by using simulated seasonal data, the said adjusted approach or procedure managed to get the desired results. We also suggest future research recommendations using advanced methods (especially when met with the ideal case or scenario).
Reference
[1] GEP Box, G.M. Jenkins, Time series analysis, forecasting and control, Golden-Day, San Francisco, 1976.
[2] L. Sachs, Applied statistics: A handbook of techniques, Second edition, Springer-Verlag, New York, 1984.
[3] D. Cochrane, G.H. Orcutt, Application of least squares regression to relationships containing auto-correlated error terms, Journal of the American Statistical Association, 44(245): 32–61.doi:10.1080/01621459.1949.10483290, 1949
[4] M.R. Spiegel, Shaum`s outline series: Theory and problems of advanced calculus: SI (Metric) edition, McGraw-Hill, Inc, 1974.
[5] K. V. Mardia, J. T. Kent, J. M. Bibby, Multivariate analysis, Academic Press Inc. (London) Ltd, 1979.
[6] J. A. Bilmes, A Gentle Tutorial of the EM algorithm and its application to parameter estimation for Gaussian Mixture and Hidden Markov Models, Technical Report, University of Berkeley, ICSI-TR-97-021, 1998.
[7] A. P. Dempster, N. M. Laird, D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal Royal Statistics Society, vol. 39, no. 1, pp. 1–38, 1977.
[8] L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, vol. 77, no. 2, pp. 257–286, 1989.
[9] T. Wang, M. Srivatsa, D. Agrawal, L. Liu, Learning, indexing, and diagnosing network faults, Proceedings of the 15th ACM SIGKDD International Conference on knowledge discovery and data mining, pp. 857–866, 2009.
[10] E. Rozaki, Design and implementation for automated network troubleshooting using data mining, International journal of data mining and knowledge management process, vol. 5., no. 3, 2015.
[11] K. S. Umadevi, R. Jagadeesh Kannan, F-MAC: An optimal clustering-based fault-tolerant system for underwater acoustic sensor networks, International Journal of Pure and Applied Mathematics, vol. 109, no. 8, pp. 177–184, 2016.
[12] M. I. M. Yusoff, I. Mohamed, M. R. Abu Bakar, Hidden Markov models: An insight, Proceedings of the 6th International Conference on Information Technology and Multimedia, doi: 10.1109/ICIMU.2014.7066641, 2014.
[13] P. Stelling, I. Foster, C. Kesselman, C. Lee, G.V. Laszewski, A fault detection service for wide-area distributed computations, Cluster Computing, vol. 2, no. 2, pp. 117–128, doi: 10.1023/A:101907040, 1999.
[14] M. Asim, H. Mokhtar, M. Merabti, A self-managing fault management mechanism for wireless sensor networks, International Journal of Wireless & Mobile Networks, vol. 2, no. 4, pp. 184–197, 2010.
[15] C.S. Hood, C. Ji, Proactive network fault detection, Proceedings of INFOCOM `97, doi: 10.1109/INFCOM.1997.631137, 1997.
[16] D.C. Montgomery, Forecasting and time series analysis, McGraw-Hill, New York, 1976.
[17] Dr. S Gajanana, Yayavaram Revanth Sai, K Rahul, S Rohith Yadav, Inflation Based replacement model for cutting tools using Markov Stochastic process, International Journal of Engineering Trends and Technology, vol. 68, no. 2, pp. 29-35, 2020.
[18] Mbamaluikem Peter O, Bitrus, Irmiya, Okeke, Henry S., Asymmetrical Fault Recognition System on Electric Power Lines Using Artificial Neural Network, International Journal of Engineering Trends and Technology, vol. 67, no. 11, pp. 61-66, 2019.
[19] Ankesh Dabhade, Rahul Kale, Saurabh Fulzele, Prof. K.S.Kalkonde, Railway Track Crack Fault Detection using Method of Histograms in Image Processing of MATLAB, International Journal of Engineering Trends and Technology (IJETT), vol. 58, no. 3, pp. 130-136, April 2018.
[20] Ayokunle A. Awelewa, Peter O. Mbamaluikem, Isaac A. Samuel, Artificial Neural Networks for Intelligent Fault Location on the 33-Kv Nigeria Transmission Line, International Journal of Engineering Trends and Technology (IJETT), vol. 54, no. 3, pp. 147-155, December 2017. [21] Fauzy Che Yayah, Khairil Imran Ghauth, Choo-Yee Ting, Adopting Big Data Analytics Strategy in Telecommunication Industry, Journal of Computer Science & Computational Mathematics, vol. 7, no. 3, pp. 57-67, 2017.
Keywords
Telecommunication network, Faults, Cochrane-Orcutt adjusted approach or procedure, Multiple regression, Autocorrelation function (ACF) plot, Partial Autocorrelation function (PACF) plot, Correlation matrix.