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استفاده از مدل شکنندگی در تخمین عمر مفید باقیمانده (مطالعه موردی: سیستم بارگیری معدن مس سونگون) | ||
| مدل سازی در مهندسی | ||
| مقاله 10، دوره 18، شماره 62، آبان 1399، صفحه 129-142 اصل مقاله (1.92 M) | ||
| نوع مقاله: مقاله مهندسی معدن | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2020.19249.1817 | ||
| نویسندگان | ||
| آوات قم قلعه* 1؛ محمد عطایی2؛ رضا خالوکاکائی3؛ عباس برآبادی4؛ علی نوری قراحسنلو5 | ||
| 1دانشجوی دکتری مهندسی استخراج معدن، دانشکده معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود، سمنان، ایران، | ||
| 2دانشکده مهندسی معدن دانشگاه صنعتی شاهرود | ||
| 3عضو هیئت‌علمی گروه معدن ، دانشگاه صنعتی شاهرود | ||
| 4استاد، عضو هیئت علمی دانشگاه شمالگان ترومسو، نروژ | ||
| 5استادیار، دانشکده فنی و مهندسی، دانشگاه بین‌المللی امام خمینی، قزوین، ایران | ||
| تاریخ دریافت: 17 آذر 1398، تاریخ بازنگری: 01 اردیبهشت 1399، تاریخ پذیرش: 11 تیر 1399 | ||
| چکیده | ||
| ایدة عمر مفید باقیمانده (RUL) مفهوم جدیدی است که پیش‌بینی از مقدار عمر مفید باقیمانده را قبل از وقوع خرابی برای یک سیستم براساس شرایط حاضر و پروفیل عملیاتی گذشته ارائه می‌دهد. در این مقاله RUL بر اساس قابلیت اطمینان تخمین زده شده و برای تقارب به نتایج واقعی‌تر تاثیر شرایط محیط عملیاتی در قالب فاکتورهای ریسک در تحلیل‌ها وارد می‌شود. این شرایط محیطی گاها ملموس و قابل مشاهده (مشهود) بوده و گاها امکان تعیین و وارد کردن آنها در تحلیل وجود نداشته و تحت عنوان "فاکتورهای ریسک نامشهود" با استفاده از مدل شکنندگی تحلیل می‌شوند. تحلیل RUL مطالعه موردی از سیستم بارگیری (شاول کوماتسو 1250) معدن مس سونگون براساس اطلاعات یک بازه 8 ماه نشان داد؛ مدل ویبول نرخ مخاطره متناسب مرکب (W-MPHM) بهترین برازش بر داده‌ها را دارد. در این مدل چهار فاکتور ریسک شیفت، نوع سنگ، نوع باربر و بارندگی با ضرایب نرخ مخاطره 66/2، 79/3، 204/0 و 18/1 به عنوان موثرترین فاکتورها بدست آمد. RUL سیستم در دو سناریوی بعد طی حدود 40 ساعت صفر شد. همچنین مقایسه این W-MPHM با مدل نمایی نرخ مخاطره متناسب (Ex-PHM) در طول 80 ساعت کارکرد نشان داد تقعر منحنی قابلیت اطمینان در مدل دومی بیشتر از مدل اولی بوده و در واقع سرعت افت قابلیت اطمینان در شروع بازه بیشتر می‌باشد. این موضوع نشان دهنده تاثیر فاکتورهای ریسک نامشهود در تحلیل علمکرد سیستم بوده و صرف نظر از آن نااریبی قابل توجهی در نتایج بدنبال خواهد شد. | ||
| کلیدواژه‌ها | ||
| ماشین‌آلات سنگین؛ قابلیت اطمینان؛ عمر مفید باقیمانده؛ مدل شکنندگی | ||
| عنوان مقاله [English] | ||
| The Application of Frailty Model in Remaining Useful Life Estimation (Case Study: Sungun Copper Mine's Loading System) | ||
| نویسندگان [English] | ||
| Awat Ghomghale1؛ Mohammad Ataei2؛ Reza Khalokakaie3؛ Abbas Barabadi4؛ Ali Nouri Qarahasanlou5 | ||
| 1Ph.D. Student of Shahrood University of Technology, Shahrood, Iran | ||
| 2Faculty of Mining, Petroleum & Geophysics, Shahrood University of Technology, Shahrood, Iran | ||
| 3Faculty of Mining, Petroleum & Geophysics, Shahrood University of Technology, Shahrood, Iran | ||
| 4UiT The Arctic University of Norway, Langnes, Tromsø, 9037, Norway | ||
| 5Assistant professor, Imam Khomeini International University, Qazvin, Iran | ||
| چکیده [English] | ||
| The Residual Useful Life (RUL) provides an estimate of the amount of remaining useful life before a system failure depends on present conditions and past operating profiles. In this paper, RUL is estimated based on reliability and for convergence to more realistic results, the effect of operating environment conditions in the form of risk factors (covariates) is also involved in the analysis. These environmental conditions are sometimes tangible and "Observable" and sometimes it is not possible to determine and include them in the analysis and are analyzed under the heading of “Un-observed risk factors” using the frailty model. RUL analysis of a case study of the loading system (Shovel Komatsu 1250) of the Sungun copper mine, based on 8-months information, has shown that Weibull mix proportional hazard (W-MPHM) has the best fit on the data. In this model, four factors including shift risk, rock kind, load system type and rainfall with risk rates of 2.66, 3.79, 0.204 and 1.18 have been obtained as the most effective factors. System RUL became zero in the next two scenarios in about 40 hours. Comparing W-MPHM and Ex-PHM over 80 hours showed that the concurrence of the reliability curve in the second model is greater than the first model and in fact, the reliability decline rate is higher at the beginning of the range. This result reflects the impact of intangible risk factors on system performance analysis, regardless of which will result in significant inconsistency of the results. | ||
| کلیدواژه‌ها [English] | ||
| Heavy Equipment, Reliability, Remaining Useful Life (RUL), Frailty model | ||
| مراجع | ||
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[1] D. Kumar, "Proportional hazards modelling of repairable systems", Quality and reliability engineering international, Vol. 11, No. 5, 1995, pp. 361–369. [2] D. Kumar and U. Westberg, "Proportional hazards modeling of time-dependent covariates using linear regression: a case study [mine power cable reliability]", Reliability, IEEE Transactions on, Vol. 45, No. 3, 1996, pp. 386–392. [3] A. Barabadi, J. Barabady and T. Markeset, "A methodology for throughput capacity analysis of a production facility considering environment condition", Reliability Engineering & System Safety, Vol. 96, No. 12, 2011, pp. 1637–1646, doi: 10.1016/j.ress.2011.09.001. [4] A. Rahadiyan Wijaya, Methods for Availability Improvements of a Scaling Machine System, Doctoral Thesis, Luleå University of Technology, Luleå, Sweden, 2012. [5] L. Lanting, Modelling Breakdown Durations in Simulation Models of Engine Assembly Lines, Thesis for the degree of Doctor of Philosophy, University of Southampton, Southampton, United Kingdom, 2009. [6] R.K. Yin, Case study research: Design and methods, Vol. 5. SAGE Publications, Incorporated, 2008. [7] R.A. Hall and L.K. Daneshmend, "Reliability Modelling of Surface Mining Equipment: Data Gathering and Analysis Methodologies", International Journal of Surface Mining, Reclamation and Environment, Vol. 17, No. 3, 2003, pp. 139–155, doi: 10.1076/ijsm.17.3.139.14773. [8] J. Barabady and U. Kumar, "Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran", Reliability Engineering & System Safety, Vol. 93, No. 4, 2008, pp. 647–653, doi: 10.1016/j.ress.2007.10.006. [9] H. Khorasgani, G. Biswas and S. Sankararaman, "Methodologies for system-level remaining useful life prediction", Reliability Engineering & System Safety, Vol. 154, 2016, pp. 8–18. [10] X. Xi, M. Chen and D. Zhou, "Remaining useful life prediction for degradation processes with memory effects", IEEE Transactions on Reliability, Vol. 66, No. 3, 2017, pp. 751–760. [11] Y. Wu, M. Yuan, S. Dong, L. Lin and Y. Liu, "Remaining useful life estimation of engineered systems using vanilla LSTM neural networks", Neurocomputing, Vol. 275, 2018, pp. 167–179. [12] A. Barabadi, Production Performance Analysis: Reliability, Maintainability and Operational Conditions, University of Stavanger, Stavanger NORWAY, 2011. [13] U. Kumar, B. Klefsjö and S. Granholm, "Reliability investigation for a fleet of load haul dump machines in a Swedish mine", Reliability Engineering & System Safety, Vol. 26, No. 4, 1989, pp. 341–361. [14] S.H. Hoseinie, M. Ataei, R. Khalokakaie, B. Ghodrati and U. Kumar, "Reliability analysis of the cable system of drum shearer using the power law process model", International Journal of Mining, Reclamation and Environment, 2012, pp. 1–15. [15] S.H. Hoseinie, M. Ataei, R. Khalokakaie, B. Ghodrati and U. Kumar, "Reliability analysis of drum shearer machine at mechanized longwall mines", Journal of quality in maintenance engineering, Vol. 18, No. 1, 2012, pp. 98–119. [16] S.H. Hoseinie, M. Ataei, R. Khalokakaie and U. Kumar, "Reliability and maintainability analysis of electrical system of drum shearers", Journal of Coal Science and Engineering (China), Vol. 17, No. 2, 2011, pp. 192–197. [17] S.H. Hoseinie, M. Ataei, R. Khalokakaie and U. Kumar, "Reliability modeling of hydraulic system of drum shearer machine", Journal of Coal Science and Engineering (China), Vol. 17, No. 4, 2011, pp. 450–456. [18] A.N. Gharahasanlou, M. Ataei, R. Khalokakaie, A. Barabadi and V. Einian, "Risk based maintenance strategy: a quantitative approach based on time-to-failure model", International Journal of System Assurance Engineering and Management, 2017, pp. 1–10, Mar. [19] A. Nouri Gharahasanlou, R. Khalokakaie, M. Khalokakaie and A. Mokhtarei, "Reliability Analysis of Conveyor Belt System of Crushing Department", JAEBS, Vol. 5, 2015, pp. 349–357. [20] A. Nouri Qarahasanlou, "Production Assurance of Mining Fleet Based on Dependability and Risk Factor (Case Study: Sungun Copper Mine)", PhD Thesis in Mineral Exploita, Shahrood University of Technology Faculty of Mining, Petroleum & Geophysics, Iran, Shahrood, 2017. [21] A. Nouri Qarahasanlou, A. Mokhtarei, A. Khodayarei and M. Ataei, "Fault tree analysis of failure cause of crushing plant and mixing bed hall at Khoy cement factory in Iran", Case Studies in Engineering Failure Analysis, Vol. 2, No. 1, 2014, pp. 33–38. [22] A. Nouri Qarahasanlou, R. Khalokakaie, M. Ataei, M. Mokhberdoran, R. Jafarei and A. Mokhtarei, "Power Law Model for Reliability Analysis of Crusher System in Khoy Cement Factory", JAEBS, Vol. 5, 2015, pp. 340–348. [23] A.H.U. Tumanggor, “Reliability value analysis of dump truck 108 unit (case study: South Kalimantan coal mining company)”, presented at the AIP Conference Proceedings, 2018, Vol. 2044, p. 020019. [24] A. Nouri Gharahasanlou, R. Khalokakaie, M. Ataei and S. Fatorachi, "Availability analysis of mining machinery in Sungun copper mine", Geotechnical Engineering Journal, Vol. 1, No. 2, 2017, pp. 61–72. [25] A. Barabadi, J. Barabady and T. Markeset, "A methodology for throughput capacity analysis of a production facility considering environment condition", Reliability Engineering & System Safety, Vol. 96, No. 12, 2011, pp. 1637–1646. [26] A. Rahadiyan Wijaya, Methods for Availability Improvements of a Scaling Machine System, Doctoral Thesis, Luleå University of Technology, Luleå, Sweden, 2012. [27] N.G. Ali, A. Mohammad, K. Reza and E. Vahid, "Throughput Capacity Analysis(Case Study: Sungun Copper Mine)", in CIVILICA, 2016, Vol. 02. [28] L. Lanting, "Modelling Breakdown Durations in Simulation Models of Engine Assembly Lines", Thesis for the degree of Doctor of Philosophy, University of Southampton, Southampton, United Kingdom, 2009. [29] R.A. Hall and L.K. Daneshmend, "Reliability Modelling of Surface Mining Equipment: Data Gathering and Analysis Methodologies", International Journal of Surface Mining, Reclamation and Environment, Vol. 17, No. 3, 2003, pp. 139–155. [30] A. Garmabaki, A. Ahmadi, J. Block, H. Pham and U. Kumar, "A reliability decision framework for multiple repairable units", Reliability Engineering & System Safety, Vol. 150, 2016, pp. 78–88. [31] J.F. Lawless, "Regression methods for Poisson process data", Journal of the American Statistical Association, Vol. 82, No. 399, 1987, pp. 808–815. [32] M.J. Brewer, A. Butler and S.L. Cooksley, "The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity", Methods in Ecology and Evolution, Vol. 7, No. 6, 2016, pp. 679–692. [33] M. Giorgio, M. Guida and G. Pulcini, "Repairable system analysis in presence of covariates and random effects", Reliability Engineering & System Safety, Vol. 131, 2014, pp. 271–281. [34] D.G. Kleinbaum and M. Klein, Survival Analysis: A Self-Learning Text, Springer Science & Business Media, 2012. [35] D.G. Kleinbaum, Survival analysis. Springer, 2011. [36] A.H.S. Garmabaki, A. Ahmadi, Y.A. Mahmood and A. Barabadi, "Reliability Modelling of Multiple Repairable Units", Quality and Reliability Engineering International, Vol. 32, No. 7, 2016, pp. 2329–2343. [37] Z.G. Asfaw and B.H. Lindqvist, "Unobserved heterogeneity in the power law nonhomogeneous Poisson process", Reliability Engineering & System Safety, Vol. 134, 2015, pp. 59–65. [38] V. Slimacek and B.H. Lindqvist, "Nonhomogeneous Poisson process with nonparametric frailty and covariates", Reliability Engineering & System Safety, Vol. 167, 2017, pp. 75–83. [39] J.H. Cha and M. Finkelstein, "Some notes on unobserved parameters (frailties) in reliability modeling", Reliability Engineering & System Safety, Vol. 123, 2014, pp. 99–103. [40] R.G. Gutierrez, "Parametric frailty and shared frailty survival models", Stata Journal, Vol. 2, No. 1, 2002, pp. 22–44. [41] C. Xiongzi, Y. Jinsong, T. Diyin and W. Yingxun, “Remaining useful life prognostic estimation for aircraft subsystems or components: A review”, presented at the Electronic Measurement & Instruments (ICEMI), 2011 10th International Conference on, 2011, Vol. 2, pp. 94–98. [42] M.-L.T. Lee and G.A. Whitmore, "Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary", Statistical Science, 2006, pp. 501–513. [43] W. Wang, "A prognosis model for wear prediction based on oil-based monitoring", Journal of the Operational Research Society, Vol. 58, No. 7, 2007, pp. 887–893.
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آمار تعداد مشاهده مقاله: 933 تعداد دریافت فایل اصل مقاله: 450 |
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