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2018 | 113 | 226-237
Article title

Computational Analysis of Screening Unit of Paper Plant with Human Error Using Neural Network Approach

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Recent approach of Neural Network can be applied to solve a wide range of optimization problems. This paper deals with reliability, non-reliability and profit analysis of a paper plant. The objective of this paper is to compute profit of paper plant using neural network Algorithm and to analyze the availability of the system. The system is divided into four main subsystems. A multi-layered neural network model is used in order to optimize the maintenance of the paper plant system. The system may fail due to hardware failure with human errors and various environmental conditions. All types of failures, repairs and waiting rates are exponential. System state probabilities and other parameters are developed for the proposed model using neural network approach. Numerical examples are included to demonstrate the results.
Physical description
  • Department of Mathematics, KIET Group of Institutions, Ghaziabad, India
  • Department of Mathematics, KIET Group of Institutions, Ghaziabad, India
  • Department of Mathematics, KIET Group of Institutions, Ghaziabad, India
  • Department of Mathematics, KIET Group of Institutions, Ghaziabad, India
  • [1] Bazovsky Igor, Reliability theory and practice, PHI Englewood cliff, NJ, 1961.
  • [2] Britler Alan L., Crystal D. Sloan, System reliability Prediction: Towards a general approach using neural network, Nashville, Tennessee, American Institute of Aeronautics and Astronautics, 2005
  • [3] Chau, K.W., Reliability and performance-based design by artificial neural network, Advances in Engineering Software, 2007, 38, pp. 145-149.
  • [4] Ekata, Gupta Neeraj and Singh S.B., Operational availability of marine vehicle system using neural network approach, International Journal of Computational Science and Mathematics, 2010, vol. 2, pp. 91-99.
  • [5] Ekata, Sharma Neelam, Batra C.M., Neural Network Approach for Analytical Study of the Reliability of Refrigeration System, Computational Intelligence on Power, Energy and Controls with their Impact on Humanity 2014 pp 511-514, IEEE Xplore.
  • [6] Karunanithi N., Whitley D., Malaiya Y.K., Using neural networks in reliability, IEEE, 1992, Vol. 9, pp. 53-59.
  • [7] Kutylowska Malgorzata, Comparison of Two Types of Artificial Neural Networks for Predicting Failure Frequency of Water Conduits. Periodica Polytechnica Civil Engineering 2017, 61(1), pp. 1–6, DOI: 10.3311/PPci.8737
  • [8] Lolas S., Olatunbosun O. A., Prediction of vehicle reliability performance using artificial neural networks, Expert systems with applications, 2008, 34, pp. 2360-2369.
  • [9] Mallikarjuna Reddy, K.L.N. Rao, Neural network for the reliability analysis of a series-parallel system subjected to common-cause and human error failures, Bulletin of Pure and Applied Sciences, 2002, vol. 21 E(no.1), pp. 251-257.
  • [10] Rajasekaran, S., Pai, G.A. Vijaylakshmi, Neural Networks, Fuzzy Logic, and Genetic Algorithms, PHI, 2004.
  • [11] Ronald E. Giutini, Mathematical characterization of human reliability for multitask system operations, IEEE, 2000, pp. 1325-39.
  • [12] Srinivasan Dipti, Choy Chee Min and Cheu Long Ruey. Neural Network for Real-Time Traffic Signal Control. IEEE Transactions on Intelligent Transportation Systems, Vol. 7, No 3, September 2006.
  • [13] Su, Yu-shen, Huang, Chin Yu, Neural –network- based approaches for software reliability estimation using dynamic weighted combinational models, The Journal of Systems are Software 2007, 80, pp. 606-6015.
  • [14] Watanade Chihiro, Hiramatsu Kaoru, Kashino Kunio, Modular Representation of layered neural networks, Neural Networks Journal 97, 2018, pp. 62-73
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