تاثیر شرایط محیطی بر قابلیت اطمینان تجهیزات معدن مطالعه موردی معدن مولیبدن-مس آذربایجان

نوع مقاله : یادداشت فنی

نویسندگان

1 دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود

2 کارشناس شرکت ملی مس ایران، واحد طراحی و نظارت معدن مس سونگون

چکیده

در صنعتی مانند صنعت معدنکاری با محیط سخت و خشن نمی‌توان از تأثیر شرایط محیطی بر رفتار سیستم‌ها صرف‌نظر کرد، چرا که این موضوع اریبی شدید در نتایج تحلیل‌ها را به دنبال خواهد داشت. با این دیدگاه در این پژوهش تلاش شد تا قابلیت اطمینان سیستم لودر از معدن مولیبدن- مس آذربایجان با استفاده از نوعی مدل رگرسیونی معروف به مدل نرخ مخاطرات متناسب محاسبه شود. این مدل از دو تابع یکی بر اساس داده‌های زمانی و دیگری شامل شرایط محیطی (فاکتورهای ریسک) هست. داده‌های مورد نیاز از بانک داده 15 ماهه از منابع مختلف چون گزارش‌های روزانه، تعمیرگاه، گزارش‌های هواشناسی، ملاقات و مشاهده‌های مستقیم و مرتب‌شده بر اساس تاریخ وقوع در قالب زمان بین خرابی‌ها و فاکتورهای ریسک استخراج گردید. در نخستین گام برای تعیین تابع پایه نرخ مخاطره، آزمون‌های روند و خودهمبستگی برای ارزیابی فرض توزیع یکسان و مستقل داده‌ها مورد استفاده قرارگرفته و در نتیجه آن فرض رد شد. لذا روش‌های کلاسیک آماری برای تحلیل نامناسب بوده و مدل فرایند قانون‌ توانی جهت تحلیل‌ سیستم تعمیرپذیر مناسب است. در مرحله بعدی نیز ضرایب رگرسیون برای فاکتورهای ریسک با سطح تأثیر معناداری مشخص و با به‌کارگیری روش بازگشتی انجام گرفت. طبق نتایج حاصل از تحلیل فاکتورهای نوبت، محل کار، تناسب با دامپتراک، وضعیت آب و هوا، دمای محیط و وضعیت جاده دارای تأثیر معناداری بودند. در نتیجه آن قابلیت اطمینان لودر بر اساس تابع زمانی و شش فاکتور ریسک محاسبه شد. در نهایت نیز اختلاف حدوداً سه برابری بهره‌وری برای دو حالت تحلیل قابلیت اطمینان با در نظرگیری فاکتورهای ریسک و بدون آنها به دست آمد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Operating Environment Based Reliability Analysis of Mining Equipment Case Study: Molybdenum-Copper Mine (Sungun Copper Mine)

نویسندگان [English]

  • Ali Nouri Qarahasanlou 1
  • Mohammad Ataei 1
  • Reza Khalokakaie 1
  • Saied Fatoorachi 2
  • Reza Barabady 1
1 Dept. of Mining, Petroleum & Geophysics, Shahrood University of Technology, Shahrood, Iran
2 Expert Technical Office of NICICO, Sungun, Iran
چکیده [English]

Summary
The proportional hazards model (PHM) that is a regression model was used to analyze the reliability of Komatsu 988 wheel loader system at Azerbaijan Molybdenum-Copper mine (Sungun Copper mine). Hazard rate in a system in PHM is the product of an arbitrary and unspecified baseline hazard rate, depending on time only, and a positive functional term, basically independent of time, incorporating the effects risk factors (covariates). The required data for PHM analysis were extracted from a database of 15 months, which collecting from different sources, such as daily reports, workshop reports, weather reports, meetings, and direct observations in format of time between failures and risk factors. At the first step, trend and serial correlation tests were used for evaluation of assumptions of independent and uniform distribution of data for determining base function of risk rate. The evaluation failed to show independency and uniform distribution. Therefore, power law process was used. In the next step, the regression coefficient of covariates were estimated. According to the results, shift, working place, proportionality of truck, weather conditions, temperature and road conditions have had a significant effect. Finally, the reliability of loaders based on the time and six risk factors was calculated, and analysis showed about three times alteration in productivity of analysis with the ignoring of risk factors compared with inserting them.
 
Introduction
The formal definition of reliability performance is ‘‘the ability of an item to perform a required function under given conditions for a given time interval’’. The ‘‘given conditions’’ are important keywords in the definition of the reliability performance concept and its related concepts. Given conditions can be such as the surrounding environment, conditions indicating parameters, the operating history of a machine, the skill of the operator or maintenance crew, etc. Thus, it is important that the statistical approach, which is used for reliability analysis, should be able to model the operational environment effect as the way it influences the failure process. There is a need for a careful and well-structured approach, access to all data and information, design and analysis tools, as well as effective design routines and procedures.
 
Methodology and Approaches
The aim of this paper is to assess the different operating environment conditions and their effect on the reliability as the two main goals. PHM was proposed in order to predict hazard rate and reliability considering the operating environment condition.
 
Results and Conclusions
The stated points in this section can be classified as follows:

The common parametric methods were reviewed
PHM for analyzing covariates is discussed
The application of this method is demonstrated using a real case study
The reliability analysis showed about three times alteration in productivity of analysis with the ignoring of risk factors compared with inserting them.

کلیدواژه‌ها [English]

  • Mining
  • Reliability
  • Risk Factors (covariates)
  • Proportional Hazards Model (PHM)
[1]           Nan, M. S., Nicolescu, C., Jula, D., Bolovan, C., Voicu, G. V., & Petre, G. (2011). Practical aspects regarding spare parts reliability evaluation within an integrated management system. International Journal of Mathematical Models and Methods in Applied Sciences, 5(2), 238–246.
[2]           Levkovich, P., & Chalenko, N. (1969). Use of reliability theory to calculate the required number of reserve longwall faces. Journal of Mining Science, 5(2), 160–165.
[3]           Al’tshuler, V. (1969). A method of constructing a mathematical model to study the reliability of mine transportation systems. Journal of Mining Science, 5(1), 72–76.
[4]           Ivko, V., Ovchinnikova, L., & Plotnikova, V. (1973). A method of estimating the operational reliability of kinematic mechanized support systems. Journal of Mining Science, 9(3), 333–335.
[5]           Freidina, E., Kovalenko, A., & Rudenko, O. (1975). Effect of mine-transport-equipment reliability on the productivity of a quarry system. Journal of Mining Science, 11(1), 50–54.
[6]           Bondar’, S., & Mernov, V. (1979). Operational reliability of conveyer lines with intermediate storage capacity. Journal of Mining Science, 15(3), 268–270.
[7]           Garakavi, A., Manevich, I., & Merkin, V. (1984). Technological reliability and its safeguards in mining operations. Journal of Mining Science, 20(6), 456–462.
[8]           Dhillon, B. S. (1986). Bibliography of literature on mining equipment reliability. Microelectronics Reliability, 26(6), 1131–1138.
[9]           Goodman, G. V. R. (1988). An assessment of coal mine escapeway reliability using fault tree analysis. Mining Science and Technology, 7(2), 205–215.
[10]         Kumar, U., Klefsjö, B., & Granholm, S. (1989). Reliability investigation for a fleet of load haul dump machines in a Swedish mine. Reliability Engineering & System Safety, 26(4), 341–361. doi:10.1016/0951-8320(89)90004-5
[11]         Kumar, U., & Klefsjö, B. (1992). Reliability analysis of hydraulic systems of LHD machines using the power law process model. Reliability Engineering & System Safety, 35(3), 217–224. doi:10.1016/0951-8320(92)90080-5
[12]         Kumar, U. (1990). Reliability Analysis of Load-Haul-Dump Machines (Phd Thesis). Lulea University of Technology, Lulea, Sweden.
[13]         Vagenas, N., Runciman, N., & Clément, S. R. (1997). A methodology for maintenance analysis of mining equipment. International Journal of Surface Mining, Reclamation and Environment, 11(1), 33–40. doi:10.1080/09208119708944053
[14]         Hall, R. A., & Daneshmend, L. K. (2003). Reliability and maintainability models for mobile underground haulage equipment. Canadian Institute of Mining, Metallurgy and Petroleum (CIM) bulletin, 96(1072), 159–165.
[15]         Vagenas, N., Kazakidis, V., Scoble, M., & Espley, S. (2003). Applying a maintenance methodology for excavation reliability. International Journal of Surface Mining, Reclamation and Environment, 17(1), 4–19.
[16]         Samanta, B., Sarkar, B., & Mukherjee, S. (2004). Reliability modelling and performance analyses of an LHD system in mining. South African Institute Mining And Metallurgy, 104, 1–8.
[17]         Barabady, J., & Kumar, U. (2008). Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran. Reliability Engineering & System Safety, 93(4), 647–653. doi:10.1016/j.ress.2007.10.006
[18]         Barabady, J., & Kumar, U. (2005). Maintenance Schedule by Using Reliability Analysis: A Case Study at Jajram Bauxite Mine of Iran (Vol. 2, pp. 831–838). Presented at the 20th World Mining Congress, Tehran, Iran: World Mining Congress.
[19]         Barabady, J. (2005). Reliability and maintainability analysis of crushing plants in Jajarm Bauxite Mine of Iran (pp. 109–115). Presented at the Reliability and Maintainability Symposium, 2005. Proceedings. Annual, IEEE.
[20]         Vayenas, N., & Wu, X. (2009). Maintenance and reliability analysis of a fleet of load-haul-dump vehicles in an underground hard rock mine. International Journal of Mining, Reclamation and Environment, 23(3), 227–238.
[21]         Hoseinie, S. H., Ataei, M., Khalokakaie, R., Ghodrati, B., & Kumar, U. (2012). Reliability analysis of the cable system of drum shearer using the power law process model. International Journal of Mining, Reclamation and Environment, 1–15. doi:10.1080/17480930.2011.622477
[22]         Hoseinie, S. H., Ataei, M., Khalokakaie, R., & Kumar, U. (2011). Reliability and maintainability analysis of electrical system of drum shearers. Journal of Coal Science and Engineering (China), 17(2), 192–197. doi:10.1007/s12404-011-0216-z
[23]         Hoseinie, S. H., Ataei, M., Khalokakaie, R., Ghodrati, B., & Kumar, U. (2012). Reliability analysis of drum shearer machine at mechanized longwall mines. Journal of quality in maintenance engineering, 18(1), 98–119.
[24]         Hoseinie, S. H., Ataei, M., Khalokakaie, R., & Kumar, U. (2011). Reliability modeling of hydraulic system of drum shearer machine. Journal of Coal Science and Engineering (China), 17(4), 450–456. doi:10.1007/s12404-011-0419-3
[25]         Gorjian Jolfaei, N. (2012). Asset health prediction using the explicit hazard model. Queensland University of Thechnology.
[26]         Ghodrati, B., & Kumar, U. (2005). Operating environment-based spare parts forecasting and logistics: a case study. International Journal of Logistics Research and Applications, 8(2), 95–105. doi:10.1080/13675560512331338189
[27]         Kumar, D., & Klefsjö, B. (1994). Proportional hazards model: a review. Reliability Engineering & System Safety, 44(2), 177–188.
[28]         Kumar, D., & Klefsjö, B. (1994). Proportional hazards model—an application to power supply cables of electric mine loaders. International Journal of Reliability, Quality and Safety Engineering, 1(03), 337–352.
[29]         Kumar, D. (1995). Proportional hazards modelling of repairable systems. Quality and reliability engineering international, 11(5), 361–369.
[30]         Kumar, D., & Westberg, U. (1996). Proportional hazards modeling of time-dependent covariates using linear regression: a case study [mine power cable reliability]. Reliability, IEEE Transactions on, 45(3), 386–392.
[31]         Prasad, P., & Rao, K. (2002). Reliability models of repairable systems considering the effect of operating conditions (pp. 503–510). Presented at the Reliability and Maintainability Symposium, 2002. Proceedings. Annual, IEEE.
[32]         Ghodrati, B., & Kumar, U. (2005). Reliability and operating environment-based spare parts estimation approach: a case study in Kiruna Mine, Sweden. Journal of Quality in Maintenance Engineering, 11(2), 169–184.
[33]         Ghodrati, B., Kumar, U., & Kumar, D. (2003). Product support logistics based on product design characteristics and operating environment (p. 21). Presented at the 38th Annual International Logistics Conference and Exhibition: SOLE 2003, Huntsville, United States: Society of Logistics Engineers.
[34]         Ghodrati, B., Banjevic, D., & Jardine, A. (2010). Developing effective spare parts estimations results in improved system availability (pp. 1–6). Presented at the Reliability and Maintainability Symposium (RAMS), 2010 Proceedings-Annual, IEEE.
[35]         Ghodrati, B., Benjevic, D., & Jardine, A. (2012). Product support improvement by considering system operating environment: A case study on spare parts procurement. International Journal of Quality & Reliability Management, 29(4), 436–450. doi:10.1108/02656711211224875
[36]         Ghodrati, B. (2006). Weibull and Exponential Renewal Models in Spare Parts Estimation: A Comparison. International Journal of Performability Engineering, 2(2), 135.
[37]         Barabadi, A., Barabady, J., & Markeset, T. (2011). A methodology for throughput capacity analysis of a production facility considering environment condition. Reliability Engineering & System Safety, 96(12), 1637–1646. doi:10.1016/j.ress.2011.09.001
[38]         Rahadiyan Wijaya, A. (2012). Methods for Availability Improvements of a Scaling Machine System (Doctoral Thesis). Luleå University of Technology, Luleå, Sweden.
[39]         IEC 60050 - International Electrotechnical Vocabulary - Details for IEV number 191-02-06: “reliability (performance).” (2014, January 24). Retrieved January 24, 2014
[40]         Yin, R. K. (2008). Case study research: Design and methods (Vol. 5). SAGE Publications, Incorporated.
[41]         IEC 60050 - International Electrotechnical Vocabulary - Details for IEV number 191-04-01: “failure.” (2014, January 24). Retrieved January 24, 2014.
[42]         Ma, Z. (2008). Survival analysis approach to reliability, survivability and prognostics and health management (phm) (pp. 1–20). Presented at the Aerospace Conference, 2008 IEEE, IEEE.
[43]         Hall, R. A., & Daneshmend, L. K. (2003). Reliability Modelling of Surface Mining Equipment: Data Gathering and Analysis Methodologies. International Journal of Surface Mining, Reclamation and Environment, 17(3), 139–155. doi:10.1076/ijsm.17.3.139.14773
[44]         Kleinbaum, D. G. (2011). Survival analysis. Springer.
[45]         Pijnenburg, M. (1991). Additive hazards models in repairable systems reliability. Reliability Engineering & System Safety, 31(3), 369–390.
[46]         Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P., & Sun, Y. (2010). The explicit hazard model-part 1: theoretical development (pp. 1–10). Presented at the Prognostics and Health Management Conference, 2010. PHM’10., IEEE.
[47]         Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), 187–220.
[48]         Martorell, S., Sanchez, A., & Serradell, V. (1999). Age-dependent reliability model considering effects of maintenance and working conditions. Reliability Engineering & System Safety, 64(1), 19–31.