Proposing a New Dynamic Maintenance Model for Reliability Improvement By Antifragility Approach: A Case Study in Iranian Gas Transmission Company-Zone10

Document Type : Original Article


1 PHD Candidate in Production and Operation Management, Industrial Management Department, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran

2 2. Gholamreza Jamali, Associate Professor in Production and Operation Management, Industrial Management Department, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran

3 Ahmad Ghorbanpour, Assistant Professor in Operation Research, Industrial Management Department, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran



Reliability is one of the most important performance evaluation indicators in maintenance and repair filed. The present study is a mixed design attempting to identify the antifragility components and their effect on the system reliability using the system dynamics. In the qualitative section, using by the thematic analysis method, with the participation of 10 organizational and academic experts, antifragility factors were identified in the form of 254 open codes, 18 organizing codes and two global codes with the review of literature and using Maxqda 2020 software. In the quantitative part of the research, the relationship between the antifragility factors with the system reliability was investigated using multiple regression method. The three criteria of learning, redundancy and exploratory discussions were identified and selected as the factors that have the highest impact on system reliability. The effect of these indicators on system reliability in a dynamic environment was simulated using the Vensim software, DDS version. The results show the positive effect of all three criteria of learning, redundancy and exploratory discussions on improving the reliability of the system in the area in gas transmission Company-zone 10. Also, the redundancy index had the highest effect and learning components and explorative discussions were in the next classes of impact on improving the system reliability.


Main Subjects

Article Title [فارسی]

ارائه مدل نوینی از نگهداری و تعمیرات پویا با رویکرد شکست‌ناپذیری در بهبود قابلیت اطمینان (مطالعه موردی: منطقه ده عملیات انتقال گاز ایران)

Authors [فارسی]

  • حمید خدری 1
  • غلامرضا جمالی 2
  • احمد قربانپور 3

1 دانشجوی دکتری مدیریت تولید و عملیات، دانشکده اقتصاد و کسب‌وکار، دانشگاه خلیج‌فارس، بوشهر، ایران

2 2. دانشیار دانشکده اقتصاد و کسب‌وکار، دانشگاه خلیج‌ فارس، بوشهر، ایران

3 استادیار دانشکده اقتصاد و کسب‌ و کار، دانشگاه خلیج‌ فارس، بوشهر، ایران

Abstract [فارسی]

قابلیت اطمینان یکی از مهم‌ترین شاخص‌های ارزیابی عملکرد در حوزه نگهداری و تعمیرات محسوب می‌شود. تحقیق حاضر که به‌صورت آمیخته انجام‌شده است به دنبال شناسایی مؤلفه‌های شکست‌ناپذیری  و بررسی تأثیر آن‌ها  بر قابلیت اطمینان سیستم  با استفاده از  پویایی سیستم‌ها  انجام‌شده است. در بخش کیفی تحقیق با استفاده از روش تحلیل مضمون، با مشارکت ۱۰ متخصص خبره سازمانی و دانشگاهی، عوامل شکست‌ناپذیری در قالب ۲۵۴ کد باز، ۱۸ کد سازمان دهنده و دو کد فراگیر با مرور ادبیات تحقیق و استفاده از نرم‌افزار ماکس کیودا[1] نسخه ۲۰۲۰ شناسایی و دسته‌بندی گردید.در ادامه و در بخش کمی تحقیق ارتباط مؤلفه‌های سازمان دهنده شکست‌ناپذیری  به روش رگرسیون چندگانه باقابلیت اطمینان سیستم موردبررسی  قرار گرفت. سه معیار یادگیری، افزونگی و بحث‌های اکتشافی به‌عنوان عواملی که بیشترین تأثیر بر قابلیت اطمینان سیستم را دارند  شناسایی و  انتخاب شدند. تأثیر این شاخص‌ها بر قابلیت اطمینان سیستم در محیطی پویا و با استفاده از نرم‌افزار ونسیم نسخه  DDS شبیه‌سازی گردید. نتایج بیانگر تأثیر مثبت هر سه معیار یادگیری، افزونگی و بحث‌های اکتشافی در بهبود قابلیت اطمینان سیستم در منطقه ده عملیات انتقال گاز است و شاخص افزونگی بیشترین تأثیر و مؤلفه‌های  یادگیری و  بحث‌های اکتشافی در رده‌های بعدی تأثیرگذاری  در بهبود قابلیت اطمینان سیستم قرار دارند.

Keywords [فارسی]

  • نگهداری و تعمیرات
  • قابلیت اطمینان
  • شکست‌ناپذیری
  • پویایی سیستم
  • انتقال گاز
  • تحلیل موضوعی
Adams, C., & Neely, A., 2000. The performance prism to boost M&A success. Measuring business excellence.
Bagdonavičius, V., & Nikulin, M., 2009. Statistical models to analyze failure, wear, fatigue, and degradation data with explanatory variables. Communications in Statistics—Theory and Methods, 38(16-17), 3031-3047.
Barlas, Y., & Carpenter, S., 1990. Philosophical roots of model validation: two paradigms. System Dynamics Review, 6(2), 148-166.
Ben-Daya, M., Kumar, U., & Murthy, D. P., 2016. Introduction to maintenance engineering: modelling, optimization and management. John Wiley & Sons.
Braun, V., & Clarke, V., 2019. Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589-597. 
Chen, F., & Wu, C., 2020. A novel methodology for forecasting gas supply reliability of natural gas pipeline systems. Frontiers in Energy, 14(2), 213-223. 2-5
Chen, N., Ye, Z. S., Xiang, Y., & Zhang, L., 2015. Condition-based maintenance using the inverse Gaussian degradation model. European Journal of Operational Research, 243(1), 190-199.
Chookah, M., Nuhi, M., & Modarres, M., 2011. A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue. Reliability Engineering & System Safety, 96(12), 1601-1610.
De Bruijn, H., Größler, A., & Videira, N., 2020. Antifragility as a design criterion for modelling dynamic systems. Systems Research and Behavioral Science, 37(1), 23-37.
Derbyshire, J., & Wright, G., 2014. Preparing for the future: development of an ‘antifragile’methodology that complements scenario planning by omitting causation. Technological Forecasting and Social Change, 82, 215-225.
Eleuteri, A., Tagliaferri, R., Milano, L., De Placido, S., & De Laurentiis, M., 2003. A novel neural network-based survival analysis model. Neural Networks, 16(5-6), 855-864.
Elsayed A., 2012. Reliability engineering.
Gaur, V., Yadav, O. P., Soni, G., & Rathore, A. P. S., 2019. A review of metrics, algorithms and methodologies for network reliability. In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1129-1133). IEEE.
Gebraeel, N., Elwany, A., & Pan, J., 2009. Residual life predictions in the absence of prior degradation knowledge. IEEE Transactions on Reliability, 58(1), 106-117.
Gopal K.Kanji, 1998. Measurement of business excellence, Total Quality Management, 9:7, 633-643,
Gorjian N., Ma L., Mittinty M., Yarlagadda P., Sun Y., 2010. A review on degradation models in reliability analysis. In: Kiritsis D., Emmanouilidis C., Koronios A., Mathew J. (eds) Engineering Asset Lifecycle Management. Springer, London.
Guest, G., MacQueen, K. M., & Namey, E. E., 2012. Introduction to applied thematic analysis. Applied thematic analysis, 3(20), 1-21.
Hao Peng, Qianmei Feng & David W. Coit, 2010. Reliability and maintenance modeling for systems subject to multiple dependent competing failure processes, IIE Transactions, 43:1, 12-22.
Kaplan, R. S., & Norton, D. P., 1992. The balanced scorecard--measures that drive performance. Harvard Business Review, 70(1), 71-79.
Keedy, E., & Feng, Q., 2012. A physics-of-failure based reliability and maintenance modeling framework for stent deployment and operation. Reliability Engineering & System Safety, 103, 94-101.
Kjell A. Doksum & Arnljot Hbyland, 1992. Models for Variable-Stress Accelerated Life Testing Experiments Based on Wener Processes and the Inverse Gaussian Distribution,  Technometrics, 34:1, 74 82.
Kobbacy, K. A., & Murthy, D. P., 2008. Complex system maintenance handbook. London: Springer.
Lawless, J., & Crowder, M., 2004. Covariates and random effects in a gamma process model with application to degradation and failure. Lifetime data analysis, 10(3), 213-227.
Li, C., Tao, L., & Yongsheng, B. , 2007. Condition residual life evaluation by support vector machine. In 2007 8th International Conference on Electronic Measurement and Instruments (pp. 4-441). IEEE.
Lu, C. J., & Meeker, W. O., 1993. Using degradation measures to estimate a time-to-failure distribution. Technometrics, 35(2), 161-174.
Ma, K., Wang, H., & Blaabjerg, F., 2016. New approaches to reliability assessment: Using physics-of-failure for prediction and design in power electronics systems. IEEE Power Electronics Magazine, 3(4), 28-41.
Mohsenijam, A., Siu, M. F. F., & Lu, M., 2017. Modified stepwise regression approach to streamlining predictive analytics for construction engineering applications. Journal of Computing in Civil Engineering, 31(3), 04016066.
Ren, Y., Cui, B., Feng, Q., Yang, D., Fan, D., Sun, B., & Li, M., 2020. A reliability evaluation method for radial multi-microgrid systems considering distribution network transmission capacity. Computers & Industrial Engineering, 139, 106145.
Saidi, L., Ali, J. B., Bechhoefer, E., & Benbouzid, M., 2017. Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Applied Acoustics, 120,1-8.
Shin, I., Lee, J., Lee, J. Y., Jung, K., Kwon, D., Youn, B. D., ... & Choi, J. H., 2018. A framework for prognostics and health management applications toward smart manufacturing systems. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(4), 535-554.
Si, W., Yang, Q., & Wu, X., 2018. Material degradation modeling and failure prediction using microstructure images. Technometrics.
Song, Y., Liu, D., Yang, C., & Peng, Y. , 2017. Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery. Microelectronics Reliability, 75, 142-153.
Sterman, J. D., 2000. Business dynamics: System thinking and modeling for a complex world Irwin McGraw-Hill. Massachusetts Institute of Technology, Engineering Systems Division: Cambridge, MA, USA.
Sterman, J. D., 2001. System dynamics modeling: tools for learning in a complex world. California management review, 43(4), 8-25.
Sterman, J., 2018. System dynamics at sixty: the path forward. System Dynamics Review, 34(1-2), 5-47.
Succi, S., 2020. Relativistic anti-fragility. The European Physical Journal Plus, 135(2), 1-7.
Taleb, N. N., 2010. The Black Swan. The Impact of the Highly Improbable. Random House Incorporated.
Taleb, N. N., 2012. Antifragile: Things that gain from disorder. Random House Incorporated.
Tseng, S. T., & Peng, C. Y., 2007. Stochastic diffusion modeling of degradation data. Journal of data Science, 5(3), 315-333.
Wang, Y., & Pham, H., 2012. Modeling the dependent competing risks with multiple degradation processes and random shock using time-varying copulas. IEEE Transactions on Reliability61(1), 13-22. [6045314].
Yadav, O. P., Choudhary, N., & Bilen, C., 2008. Complex system reliability estimation methodology in the absence of failure data. Quality and reliability engineering international, 24(7), 745-764.
Yu, J., Zheng, S., Pham, H., & Chen, T., 2018. Reliability modeling of multi‐state degraded repairable systems and its applications to automotive systems. Quality and Reliability Engineering International, 34(3), 459-474.
Yu, W., Wen, K., Min, Y., He, L., Huang, W., & Gong, J., 2018. A methodology to quantify the gas supply capacity of natural gas transmission pipeline system using reliability theory. Reliability Engineering & System Safety, 175, 128-141.
Zhang, F., & Shi, Y., 2020. Geometry on the statistical manifold induced by the degradation model with soft failure data. Journal of Computational and Applied Mathematics, 363, 211-222.
Zhang, X., Wang, B., & Chen, X., 2015. Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowledge-Based Systems, 89, 56-85
Zhou, Q., & Thai, V. V., 2016. Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction. Safety science, 83, 74-79.
Zhu, S. P., Huang, H. Z., Peng, W., Wang, H. K., & Mahadevan, S., 2016. Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty. Reliability Engineering & System Safety, 146, 1-12.
Zio, E., 2016. Some challenges and opportunities in reliability engineering. IEEE Transactions on Reliability, 65(4),1769-1782.