On human to artificial neural network in maintenance applications

  • Authors

    • P. Ozor 1. University of Johannesburg, South Africa2. University of Nigeria Nsukka, Nigeria
    • S. O. Onyegegbu
    • J. C. Agunwamba
    • C. Mbohwa
    2019-04-12
    https://doi.org/10.14419/ijet.v7i4.19077
  • Artificial Neural Network, Human Neural Network, Maintenance, Reliability Prediction, Repairable System.

  • Abstract

    Maintenance is key to meeting sustainability and productivity expectations of repairable industrial systems. The upsurge of growth in complexity of processing equipment has since raised the maintenance function beyond the comprehension of human neural networks, thereby necessitating recourse to artificial intelligence techniques. The nexus between human neural network and artificial neural network was x-rayed in this paper. Attempt was made to present the foundations of artificial neural network modeling techniques and the major contributors to the development of the field. Some of the terminologies associated with human and artificial neural networks were defined and explained with relevant figures. The capability of artificial neural network in predicting the reliability of a case study industrial system, with data taken from a beverage company was illustrated. The two-layer network has one input and output layer apiece, as well as a hidden layer of twenty neurons. The architecture is a feed forward back propagation neural network, while the Levemberg-Marquart training algorithm was employed. It was observed that reliability predictions obtained from ANN model proved very good, considering the very low values of MSE (1.0256X10-3) and high value of the regression (0.998742).

     

     

  • References

    1. [1] P.A., Ozor, S.O. Onyegegbu, J.C. Agunwamba, Modeling composite performance variable of deteriorating systems using empirical evidence and artificial neural network, International Journal of Reliability and Safety Vol. 11 No. 1/2 (2017) 23-50. https://doi.org/10.1504/IJRS.2017.088546.

      [2] E. Ruschel, E.A.P. Santos, E.F.R. Loures, Industrial maintenance decision making: A systematic literature review, Journal of Manufacturing Systems 45 (2017) 180–194. https://doi.org/10.1016/j.jmsy.2017.09.003.

      [3] N. Salari, V. Makis, Comparison of two maintenance policies for a multi-unit system considering production and demand rates, International Journal of Production Economics 193 (2017) 381–391. https://doi.org/10.1016/j.ijpe.2017.08.003.

      [4] H.Z. Wang, H. Pham, Reliability and Optimal maintenance, Springer Series in Reliability Engineering, Springer-Verlag London Limited (2005) 75-90.

      [5] A. Alrabghi, A., Tiwari, M., Savill, Simulation-based optimization of maintenance systems: Industrial case studies Journal of Manufacturing Systems 44 (2017) 191–206. https://doi.org/10.1016/j.jmsy.2017.05.008.

      [6] A.H.S. Garmabaki, A. Ahmadi, J. Block, H. Pham, U. Kumar, A reliability decision framework for multiple repairable units, Reliability Engineering and System Safety 150 (2016) 78–88. https://doi.org/10.1016/j.ress.2016.01.020.

      [7] A. Rohani, H. M. Abbaspour-Fard S. Abdolahpour, Prediction of tractor repair and maintenance costs using Artificial Neural Network, Expert Systems with Applications 38 (2011) 8999–9007. https://doi.org/10.1016/j.eswa.2011.01.118.

      [8] K. Swingler, Applying Neural Network: A Practical Guide, Morgan Kaufman Publishers, Inc, San Francisco, California (2001) 85-100

      [9] Anon 1, Human nervous system in tamil. http://www.myavchiropractor.com/wp-content/uploads/2016/04/Peripheral-nervous-system.jpg, Retrieved on 03/13, (2018).

      [10] M.T. Hagan, H.B., Demuth, M. Beale, Neural Network Design, PWS Publishing Company, Thomson Learning, USA (1996) 130-135.

      [11] S. Haykin, Neural Networks: A Comprehensive Foundation, Second edition, Pearson Prentice Hall, New Jersey (1999) 70-105

      [12] A.A. Chojaczyk, A.P. Teixeira, L.C. Neves, J.B. Cardoso, S. C. Guedes, Review and application of Artificial Neural Networks models in reliability analysis of steel structures, Structural Safety 52 (2015) 78–89. https://doi.org/10.1016/j.strusafe.2014.09.002.

      [13] Anon 2, Images. https://en.wikibooks.org/wiki/Artificial_Neural_Networks/History, Retrieved on 03/13/ 2018.

      [14] W. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull Math Biophysics 5, (1943), 115–33. https://doi.org/10.1007/BF02478259.

      [15] D.O. Hebb, the Organization of Behaviour: A Neuropsychological Theory. New York, Wiley (1949), 20-28.

      [16] F. Rosenblatt, the Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Psychological Review, 65 (1958) 386-408. https://doi.org/10.1037/h0042519.

      [17] B. Widrow, M.E. Hoff, Adaptive Switching Circuits, IRE WESCON Convention Record, New York: IRE part 4, (1960), 96-104.

      [18] M.L. Minskey, S.A. Papert, Perceptrons, Cambridge, MA, MT press, NJ, (1969) 5-7.

      [19] S. Grossberg, Adaptive Pattern Classification and Universal Recoding: Parallel Development and Coding of Neural Feature Detectors, Biological Cybernetics, 23 (1976) 121-134. https://doi.org/10.1007/BF00344744.

      [20] T. Kohonen, Correlation Matrix Memories, IEEE Transactions on computers, 21 (1972) 353-359. https://doi.org/10.1109/TC.1972.5008975.

      [21] J.A. Anderson, A Simple Neural Network Generating an Interactive Memory, Mathematical Biosciences, 14 (1972) 197-220. https://doi.org/10.1016/0025-5564(72)90075-2.

      [22] J.J. Hopfield, Neural Networks and Physical Systems with Emergent Collective Computational Properties, Proceedings of the National Academy of Sciences, 79 (1982) 2554-2558.

      [23] D.E. Rumelhart, S. McClelland, eds. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, V.1 Cambridge, MA: MIT press (1986) 7-11.

      [24] C.E. Ebeling, an Introduction to Reliability and Maintainability Engineering, New Delhi: Tata McGraw Hill Publishing Company Limited (1997) 97-103.

      [25] A. C. Odele, RAM informed Decision Making: A Case Study of Nigeria Breweries PLC, AMA, Thesis Submitted to the Department of Mechanical Engineering, University of Nigeria Nsukka (Unpublished) (2013).

      [26] T. Pang-Ning, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison Wesley, Boston, MA (2005) 30-39.

      [27] G.I. Parisi, J. Tani, C. Weber, S. Wermter, Lifelong learning of human actions with deep neural network self-organization, Neural Networks 96 (2017) 137–149. https://doi.org/10.1016/j.neunet.2017.09.001.

      [28] J. Roell, Understanding Recurrent Neural Networks: The Preferred Neural Network for Time-Series Data, Available online: https://towardsdatascience.com/understanding-recurrent-neural-networks-the-prefered-neural-network-for-time-series-data-7d856c21b759, Retrieved on 06/2017.

      [29] Anon 4, Self Organizing Map AI for pictures: http://www.mperfect.net/aisompic/Retrieved on 27/10/2017.

      [30] H. Demuth, M. Beale, Neural Network Toolbox, for Use with MATLAB® User’s Guide, version 4 (2000) 136-142.

      [31] H.M. Beale, M.T. Hagan, H.B. Demuth, Neural Network Toolbox™ User’s Guide, The MathWorks Inc. Natick, MA 01760-2098 (2012b) 92-97.

      [32] A.H Kramer, V. A. Sangiovanni, Efficient Parallel Learning Algorithms for Neural Networks, In Advances in Neural Information Processing Systems 1 (D.S. Touretzky Edition), San Mateo, CA,Morgan Kaufmann. 40-78 (1989) 170-176.

      [33] F. Jia, Y. Lei, J. Lin, X. Zhou, N. Lu, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mechanical Systems and Signal Processing 72-73, (2016) 303–315. https://doi.org/10.1016/j.ymssp.2015.10.025.

      [34] M.A. Hakeem, M. Kamil, Analysis of artificial neural network in prediction of circulation rate for a natural circulation vertical thermosiphon reboiler, Applied Thermal Engineering 112 (2017) 1057–1069. https://doi.org/10.1016/j.applthermaleng.2016.10.119.

      [35] P.A. Ozor, Application of Artificial Neural Network to Multivariate Industrial Maintenance Systems, Master’s Degree thesis submitted to the Department of Mechanical Engineering, University of Nigeria, Nsukka (Unpublished) (2015).

      [36] J.Z. Sikorska, M. Hodkiewicz, L. Ma, Prognostic Modeling Options for Remaining Useful Life Estimation by Industry, Mechanical Systems and Signal Processing, 25 (2010) 1803-1836.

  • Downloads

  • How to Cite

    Ozor, P., O. Onyegegbu, S., C. Agunwamba, J., & Mbohwa, C. (2019). On human to artificial neural network in maintenance applications. International Journal of Engineering & Technology, 7(4), 5683-5689. https://doi.org/10.14419/ijet.v7i4.19077

    Received date: 2018-09-09

    Accepted date: 2019-03-26

    Published date: 2019-04-12