Document Type: Case Report


1 Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur,Iran

2 Computer Engineering Department Ferdowsi University of Mashhad Mashhad, Iran


Telecommunication companies have received a great deal of research attention, which have many advantages such as low cost, higher qualification, simple installation and maintenance, and high reliability. However, the using of technical maintenance approaches in Telecommunication companies could improve system reliability and users' satisfaction from Asymmetric digital subscriber line (ADSL) services. In ADSL systems, there are many variables giving some noise for classification and there are many fault patterns with overlapping data. Therefore, this paper proposes a multilayer perceptron (MLP) classifier integrated with Self Organization Map (SOM) models for fault detection and diagnosis (FDD) of occurred ADSL systems. The interest of this paper is to improve the performance of single MLP by dividing the fault pattern space into a few smaller sub-spaces using SOM clustering technique and triggering the right local classifier by designing a supervisor agent. The performances of this method are evaluated on the fault data of Iranian Telecommunication Company which develop ADSL services and then the proposed algorithm is also compared against single MLP. Finally, the results obtained by this algorithm are analyzed to increase user's satisfaction with reducing occurred faults for them with predicting before they face it.


Main Subjects

1 McKay, H.: ‘Development of the contractual relationship between an ISP and its customers–Is a fairer deal in sight?’, Computer Law & Security Review, 2005, 21, (3), pp. 216-225
2 Carrera, أ., Iglesias, C.A., Garcأa-Algarra, J., and Kolaإ™أk, D.a.: ‘A real-life application of multi-agent systems for fault diagnosis in the provision of an Internet business service’, Journal of Network and Computer Applications, 2014, 37, pp. 146-154
3 Hanafizadeh, P., and Mirzazadeh, M.: ‘Visualizing market segmentation using self-organizing maps and Fuzzy Delphi method–ADSL market of a telecommunication company’, Expert Systems with Applications, 2011, 38, (1), pp. 198-205
4 Gao, Y., Liu, S., Li, F., and Liu, Z.: ‘Fault detection and diagnosis method for cooling dehumidifier based on LS-SVM NARX model’, International Journal of Refrigeration, 2015
5 Muralidharan, V., Sugumaran, V., and Indira, V.: ‘Fault diagnosis of monoblock centrifugal pump using SVM’, Engineering Science and Technology, an International Journal, 2014, 17, (3), pp. 152-157
6 Hakim, S.J.S., Razak, H.A., and Ravanfar, S.A.: ‘Fault diagnosis on beam-like structures from modal parameters using artificial neural networks’, Measurement, 2015, 76, pp. 45-61
7 Lashkari, N., Poshtan, J., and Azgomi, H.F.: ‘Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks’, ISA Transactions, 2015
8 Lau, C.K., Ghosh, K., Hussain, M.A., and Hassan, C.R.C.: ‘Fault diagnosis of Tennessee Eastman process with multi-scale PCA and ANFIS’, Chemometrics and Intelligent Laboratory Systems, 2013, 120, pp. 1-14
9 Mnassri, B., and Ouladsine, M.: ‘Reconstruction-based contribution approaches for improved fault diagnosis using principal component analysis’, Journal of Process Control, 2015, 33, pp. 60-76
10 Huang, J., and Yan, X.: ‘Dynamic process fault detection and diagnosis based on dynamic principal component analysis, dynamic independent component analysis and Bayesian inference’, Chemometrics and Intelligent Laboratory Systems, 2015
11 Zhao, Y., Wen, J., and Wang, S.: ‘Diagnostic Bayesian networks for diagnosing air handling units faults–Part II: Faults in coils and sensors’, Applied Thermal Engineering, 2015, 90, pp. 145-157
12 Zhang, Z., and Dong, F.: ‘Fault detection and diagnosis for missing data systems with a three time-slice dynamic Bayesian network approach’, Chemometrics and Intelligent Laboratory Systems, 2014, 138, pp. 30-40
13 Eslamloueyan, R.: ‘Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee–Eastman process’, Applied soft computing, 2011, 11, (1), pp. 1407-1415
14 Rad, M.A.A., and Yazdanpanah, M.J.: ‘Designing supervised local neural network classifiers based on EM clustering for fault diagnosis of Tennessee Eastman process’, Chemometrics and Intelligent Laboratory Systems, 2015
15 Mendoza, O., Melأ­n, P., and Castillo, O.: ‘Interval type-2 fuzzy logic and modular neural networks for face recognition applications’, Applied soft computing, 2009, 9, (4), pp. 1377-1387
16 Sanchez, D., Melin, P., and Castillo, O.: ‘Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition’, Information Sciences, 2015, 309, pp. 73-101
17 Sanchez, D., and Melin, P.: ‘Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure’, Engineering Applications of Artificial Intelligence, 2014, 27, pp. 41-56
18 Chicco, G.: ‘Overview and performance assessment of the clustering methods for electrical load pattern grouping’, Energy, 2012, 42, (1), pp. 68-80
19 Lopez, J.J., Aguado, J.A., Martin, F., Munoz, F., Rodriguez, A., and Ruiz, J.E.: ‘Hopfield K-Means clustering algorithm: A proposal for the segmentation of electricity customers’, Electric Power Systems Research, 2011, 81, (2), pp. 716-724
20 Guerrero, J.D.M., Marcelli, D., Soria-Olivas, E., Mari, F., Martinez-Martأnez, J.M., Bech, I.S., Martinez-Sober, M., Scatizzi, L., Gimez-Sanchis, J., and Stopper, A.: ‘Self-Organising Maps: A new way to screen the level of satisfaction of dialysis patients’, Expert systems with applications, 2012, 39, (10), pp. 8793-8798
21 Segev, A., and Kantola, J.: ‘Identification of trends from patents using self-organizing maps’, Expert systems with applications, 2012, 39, (18), pp. 13235-13242
22 Davies, D.L., and Bouldin, D.W.: ‘A cluster separation measure’, Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1979, (2), pp. 224-227
23 Szymczyk, P., and Szymczyk, M.: ‘Classification of geological structure using ground penetrating radar and Laplace transform artificial neural networks’, Neurocomputing, 2015, 148, pp. 354-362
24 Prakash, M., Pradhan, S., and Roy, S.: ‘Soft computing techniques for fault detection in power distribution systems: A review’, in Editor (Ed.)^(Eds.): ‘Book Soft computing techniques for fault detection in power distribution systems: A review’ (IEEE, 2014, edn.), pp. 1-6
25 Chojaczyk, A.A., Teixeira, A.P., Neves, L.C., Cardoso, J.B., and Soares, C.G.: ‘Review and application of Artificial Neural Networks models in reliability analysis of steel structures’, Structural Safety, 2015, 52, pp. 78-89
26 Palanichamy, A., Jayas, D.S., and Holley, R.A.: ‘Predicting survival of Escherichia coli O157: H7 in dry fermented sausage using artificial neural networks’, Journal of Food Protectionآ®, 2008, 71, (1), pp. 6-12
27 Gosukonda, R., Mahapatra, A.K., Liu, X., and Kannan, G.: ‘Application of artificial neural network to predict< i> Escherichia coli</i> O157: H7 inactivation on beef surfaces’, Food Control, 2014, 47, pp. 606-614
28 Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., and Dai, K.: ‘An efficient intrusion detection system based on support vector machines and gradually feature removal method’, Expert Systems with Applications, 2012, 39, pp. 424–430
29 Esa Alhoniemi, J.H., Jukka Parviainen and Juha Vesanto: ‘Som toolbox for matlab’, in Editor (Ed.)^(Eds.): ‘Book Som toolbox for matlab’ (1999, edn.), pp.
30 Vesanto, J., Himberg, J., Alhoniemi, E., and Parhankangas, J.: ‘Self-organizing map in Matlab: the SOM Toolbox’, in Editor (Ed.)^(Eds.): ‘Book Self-organizing map in Matlab: the SOM Toolbox’ (1999, edn.), pp. 16-17
31 Kuzmanovskia., I., Trpkovskaa., M., and Soptrajanov, B.: ‘Optimization of supervised self-organizing maps with genetic algorithms for classification of urinary calculi’, Journal of Molecular Structure, 2005, 744–747, pp. 833–838