Radial Basis Neural Network for Availability Analysis
The appliance of radial basis neural network is demostrated in this paper. The method applies failure and repair rate signals to learn the hidden relationship presented into the input pattern. Statistics of availability of several years is considered and collected from the management of concern plant. This data is considered to train and calidate the radial basis neural network (RBNN). Subsequently validated RBNN is used to estimate the availability of concern plant. The main objective of using neural network approach is that it’s not require assumption, nor explicit coding of the problem and also not require the complete knowledge of interdependencies, only requirement is raw data of system functioning.
Arora, N. and Kumar, D. (1997). “Availability analysis of steam and power generation systems in the thermal power plant”, Microelectron Reliability, 37, 5, 795–799.
Aven, T. (1990). “Availability Formulae for Standby systems of Similar Units that are Preventively Maintained”,IEEE Transaction on Reliability, 39, 5, 603–606. https://doi.org/10.1109/24.61319
Bhattacharya, S. and Singh S. S. (2011). “Location Prediction using Efficient Radial Basis Neural Network”, International conference on Information and Network Technology, IACSIT Press, Singapore, 4, 68–72.
Broomhead, D. S. and Lowe, D. (1988). “Multivariate functional interpolation and adaptive networks”,Complex Systems, 2, 321–355.
Chen, S., Cowan, C. F. N. and Grant, P. M. (1991). “Orthognal least square learning algorithm for radial basis function network”, IEEE Trans. On neural networks, 2, 2, 302–309. https://doi.org/10.1109/72.80341
Dyer, D. (1989). “Unification of reliability, availability, and reparability models for Markov systems”, IEEE Transactions on Reliability, 38 2, 246–252. https://doi.org/10.1109/24.31116
Garg, D., Kajal, S., Singh, J. and Kumar, K. (2008). “Performance Analysis of a Screw Plant”, Journal of Mathematics and Systems Science, 4, 85–94.
Garg, D. and Kumar, K. (2009). “Performance Analysis of a Cattel Feed Plant”, ICFAI Journal of Science & Technology, 5 2, 83–94.
Garg, D. and Kumar, K. (2009). “Matrix Based System Reliability Method and its application to Rice Plant”,ICFAI Journal of Computational Mathematics, 2 4, 17–30.
Garg, D. and Kumar, K. (2009). “Availibility Analysis of a Cattle Feed Plant using Matlab Tool”, International Journal of Applied Engineering Research, 4 6, 913–920.
Garg, D., Kajal, S., Singh, J. and Kumar, K. (2009). “Reliability Analysis of a Screw Plant Using Matrix Method”, Ganita Sandesh, 23 1, 71–78.
Garg, D., Singh, J. and Kumar, K. (2009). “Availibility Analysis of a Cattle Feed Plant Using Matrix Method”,International Journal of Engineering, 3, 2, 201–219.
Garg, D., Singh, J. and Kumar, K. (2009). “Performance Analysis of Screw Plant Using Matlab Tool”,International Journal of Industrial Engineering Practice, 1, 2, 155–159.
Gupta, P., Lal, A. K., Sharma, R. K. and Singh, J. (2005). “Behavioral Study of the Cement sciences”, Journal of Mathematics and Systems Sciences, 1, 1, 50–70.
Gupta, P., Lal, A. K., Sharma, R. and Singh, J. (2007). “Analysis of reliability and availability of the serial processes of plastic –pipe manufacturing plant: A case study”, International Journal of Quality & Reliability Management, 24 4, 404–419. https://doi.org/10.1108/02656710710740563
Hartman, E. J., Keeler, J. D. and Kowalski, J. M. (1990). “Layered neural networks with Gaussian hidden units as universal approximators”, Neural Comput., 2, 210–215. https://doi.org/10.1162/neco.19220.127.116.11
Kaushik, S. and Singh, I. P. (1994). “Reliability analysis of the naphtha fuel oil system in a thermal power plant”,Microelectronics.Reliability, 34, 2, 369–372. https://doi.org/10.1016/0026-2714(94)90119-8
Kumar, D., Singh, J. and Pandey, P. C. (1990). “Design and cost analysis of a refining system in the sugar industry”,Microelectronics Reliability, 30, No 6, 1025–1028. https://doi.org/10.1016/0026-2714(90)90274-Q
Kumar, S., Mehta, N. P. and Kumar, D. (1997). “Steady State behavior and maintenance planning of a desulphurization system in a urea fertilizer plant”,Microelectronics Reliability, 37, 6, 949–953. https://doi.org/10.1016/0026-2714(95)00115-8
Markopoulos, A. P., Georgiopoulos, S. and Manolakos, D. E. (2016). “On the use of back propagation and radial basis function neural networks in surface roughness prediction”, Journal of Industrial Engineering International, 12, 3, 389–400. https://doi.org/10.1007/s40092-016-0146-x
Mishra, S. K. and Sharma, N. (2018). “Rainfall Forecasting using Back Propagation Neural Network”, Innovations in Computational Intelligence. Studies in Computational Intelligence, Chapter 19, 713, 277–288.
Park, J. and Sandberg, J. W. (1991). “Universal approximation using radial basis function networks”,Neural computation, 13, 246–251. https://doi.org/10.1162/neco.1918.104.22.168
Poggio, T. and Girosi, F. (1990). “Networks for approximation and learning”, Proc. IEEE, 78, 9, 1481–1497. https://doi.org/10.1109/5.58326
Powell, M., Mason, J. C. and Cox, M. G. (1987). “Radial basis functions for multivariable interpolation: A review, In Algorithms For Approximation”, Clarendon Press Institute Of Mathematics And Its Applications Conference Series. Clarendon Press, New York, NY, 143–167.
Singh, I. P., Tewari, P. C. and Khare, M. K. (1991).“Reliability analysis of a conveyer belt system, with only one server, subject to failures and idleness after repair”, Microelectronics Reliability, 31, 5, 823–826. https://doi.org/10.1016/0026-2714(91)90018-3
Singh, J. and Dayal, B. (1992). “Reliability Analysis of A system In a Fluctuating Environment”,Microelectronics Reliability, 32, 5, 601–603. https://doi.org/10.1016/0026-2714(92)90612-O
Singh, J. and Goel, P. (1995). “Availability analysis of a standby complex system having imperfect switch -over device”, Microelectronics Reliability, 35, 2, 285–288. https://doi.org/10.1016/0026-2714(94)00041-L
Tatar, A., Naseri, S., Sirach, N. (2015). Moonyong, L. and Bahadori, A., “Prediction of reservoir brine properties using radial basis function (RBF) neural network”, Petroleum, 1, 349–357. https://doi.org/10.1016/j.petlm.2015.10.011
Tewari, P. C., Kumar, D., Kajal, S. and Khanduja, R. (2015). “Decision support system for the cystallization unit of a sugar plant”, Icfai J. of Science and Technology, 4, 7–16.
Venkatesan, P. and Anithe, S. (2016). “Application of a radial basis function neural network for diagnosis of diabetics mellitum”, Current Science, 91, 9, 1195–1199.
Copyright (c) 2019 Mathematical Journal of Interdisciplinary Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
License and Copyright Policy
Chitkara University Publications for the journal (Math. J. Interdiscip. Sci.) protects author rights e.g., the results, analysis, methodology of Theoretical calculations or experiment. The copyright transfer form with open access policy under the creative common licenses of journal provides all rights specifically to the author (s); except to sell, distribution of the material in any form to any third party. Also, the authors are encouraged to submit the author’s copy of the accepted paper to an appropriate archive e.g. arxive.org and/or in their institution’s repositories, or on their personal website also.
Author(s) should mention reference of the Journal of Chitkara University Publications and DOI number of the publication carefully on the required page of the depository, in all above-mentioned cases. The copyright and license policy of Chitkara University Publications not only protect the author's rights but also protect the integrity and authenticity of the scientific records and takes very seriously about the plagiarism, fraud or ethics disputes.
Articles in Mathematical Journal of Interdisciplinary Sciences (Math. J. Interdiscip. Sci.) by Chitkara University Publications are Open Access articles that are published with licensed under a Creative Commons Attribution- CC-BY 4.0 International License. Based on a work at https://mjis.chitkara.edu.in. This license permits one to use, remix, tweak and reproduction in any medium, even commercially provided one give credit for the original creation.
View Legal Code of the above mentioned license, https://creativecommons.org/licenses/by/4.0/legalcode
View Licence Deed here https://creativecommons.org/licenses/by/4.0/
|Mathematical Journal of Interdisciplinary Sciences by Chitkara University Publications is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at https://mjis.chitkara.edu.in|