Designing an intelligent dynamic model of preventive maintenance and repairs in the textile and clothing industry in interaction with production using fuzzy-artificial neural network.

Document Type : Original Article


1 South Tehran Branch, Islamic Azad University, Tehran , Iran

2 Islamic Azad University Science and Research Branch

3 Science and Research Branch, Islamic Azad University, Tehran, Iran

4 Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran.


One of the issues that cause problems for industries today, especially in the field of sales, is the cost of repairing equipment breakdowns and, as a result, out of service. Specific to that organization, therefore, it should be done by the managers and researchers of that group. Conducting the research "Designing a dynamic intelligent model of preventive maintenance and repairs using the methodology of artificial nervous system - fuzzy logic and in interaction with the production" of the documents of textile and clothing industries in Borujerd, where the project is implemented and on a sample of 2000 of them, the simulation is Vansim's help and then using the artificial intelligence environment of MATLAB software can be claimed. that if (If); situation "Technology factor in Net" should be good, that is 0.9129; and the situation"employees in the net" is good (the upper limit of the membership function is good), that is, 0.9239; And the status of "working environment factor on the net" is relatively good, that is, exactly 0.8859; And the status of should be completel


Main Subjects

  1. Alsyouf, I. 2009. Maintenance practices in Swedish industries: Survey results. International Journal of Production Economics, 121 (1), 212-223. doi:

    Arnetz, J. E., Zhdanova, L., & Arnetz, B. B. (2016). Patient involvement: a new source of stress in health care work? Health communication, 31(12), 1566-1572.

    Bangalore, P., & Tjernberg, L. B. 2015. An artificial neural network approach for early fault detection of gearbox bearings. IEEE Transactions on Smart Grid, 6 (2), 980-987.

    Bell, M. A. 2015. Methods for enhancing system dynamics modeling: state-space models, data-driven structural validation & discrete event simulation. (PhD), Lancaster University.

    Brailsford, S., Desai, S. M., & Viana, J. 2010. Towards the holygrail: Combining System dynamics and discrete-event simulation in healthcare. Paper presented at the Proceedings of the Winter Simulation Conference

    Droguett, E. L., Jacinto, C. M. C., Garcia, P. A. A., & Moura, M. 2006. Availability assessment of onshore oil fields. Paper presented at the Proceedings of the European Safety and Reliability Conference 2006, ESREL 2006 – Safety and Reliability for Managing Risk.

    Eti, M.C. & Ogaji, Stephen & Probert, S.D.. (2006). Reducing the cost of preventive maintenance (PM) through adopting a proactive reliability-focused culture. Applied Energy. 83. 1235-1248.

    Garcia, Kyounghyun, Minh Chau Nguyen, and Heesun Won. 2015. "Web-based collaborative big data analytics on big data as a service platform." 17th International Conference on Advanced Communication Technology

    Kauppi, K, Longoni, A. 2016. Managing country disruption risks and improving operational performance: risk management along integrated supply chains, International Journal of Int.J. Production Economics, vol. 182, PP.484-495.

    Kováč, J., Stejskal, T., & Valenčík, Š. (2013). Virtual Reality in the Maintenance of Machinery and Equipment. Applied Mechanics and Materials, 282, 269–273.

    Laks, Paul & Wim J. C. Verhagen. 2018. Identification of optimal preventive maintenance decisions for composite components. Transportation Research Procedia, Volume 29, 2018, Pages 202-212

    Liyanage, J. and Kumar, U. (2003), "Towards a value‐based view on operations and maintenance performance management", Journal of Quality in Maintenance Engineering, Vol. 9 No. 4, pp. 333-350.

    Sgarbossa, Fabio, 2018. Impacts of Weibull parameters estimation on preventive maintenance cost. IFAC-PapersOnLine, Volume 51, Issue 11, 2018, Pages 508-513

    1. K. Yang, "A condition-based failure-prediction and processing-scheme for preventive maintenance," in IEEE Transactions on Reliability, vol. 52, no. 3, pp. 373-383, Sept. 2003.

    Song, Jian, et al. 2018. Dynamic Simulation of the Group Behavior under Fire Accidents Based on System Dynamics. Procedia Engineering, Volume 211, 2018, Pages 635-643

    Trejo, d. and Reinschmidt, k. \Justifying materials selection for reinforced concrete structures: Part I – Sensitivity analysis", Journal of Bridge Engineering, 12(1), pp. 31-37 (2005).

    Wan, Shan & Li, Dongbo & Gao, James & Roy, Rajkumar & He, Fei. (2018). A collaborative machine tool maintenance planning system based on content management technologies. The International Journal of Advanced Manufacturing Technology. 94. 1639-1653. 10.1007/s00170-016-9829-0.

    Woodhouse P., & Tjernberg, L. B. 2015. An artificial neural network approach for early fault detection of gearbox bearings. IEEE Transactions on Smart Grid, 6 (2), 980-987.

    Xue, Chaogai &Yawen Xu. 2017. Influence Factor Analysis of Enterprise IT Innovation Capacity Based on System Dynamics. Procedia Engineering, Volume 174, 2017, Pages 232-239