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  • ارایه یک رویکرد جدید به منظور افزایش اثربخشی و انتخاب بحرانی ترین تجهیز به کمک رویکرد تلفیقی Data Mining, Fuzzy DEMATEL, FMEA و FTA (مطالعه موردی: پتروشیمی شازند)

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کد مقاله : 536801 بازدید : 64 صفحه: 1 - 12

نوع مقاله: پژوهشی

ارایه یک رویکرد جدید به منظور افزایش اثربخشی و انتخاب بحرانی ترین تجهیز به کمک رویکرد تلفیقی Data Mining, Fuzzy DEMATEL, FMEA و FTA (مطالعه موردی: پتروشیمی شازند)

محورهای موضوعی : مدیریت صنعتی

Mohammad Ehsanifar 1 (Assistant Professor, Department of Industrial Engineering, Islamic Azad University, Arak branch, Arak, Iran)
Nima Hamta 2 (Assistant Professor, Department of Mechanical Engineering, Arak University of Technology, Arak, Iran)
Parisa Bolhasani 3 (MSc Graduate, Department of Industrial Engineering, Islamic Azad University, Arak branch, Arak, Iran)

تاریخ دریافت : 1396/06/08 تاریخ پذیرش : 1396/09/01 تاریخ انتشار : 1396/10/03

کلید واژه: Data mining, Maintenance Planning, Reliability Improvement, Failure Modes and Effects Analysis, داده کاوی, Preventive maintenance, نگهداری و تعمیرات پیشگیرانه, برنامه ریزی تعمیرات, بهبود قابلیت اطمینان, تجزیه و تحلیل حالات خطا, درخت خطا, Fault Tree Analysis,

چکیده مقاله :

طی دهه های اخیر به دلایل زیادی، شاهد توجه رو به رشدی به مقوله قابلیت اطمینان و مدیریت نگهداری و تعمیرات بوده‌ایم. مقاله حاضر با هدف معرفی و تلفیق رویکردهای داده کاوی، دیمتل فازی، تجزیه و تحلیل حالات خطا و درخت خطا به منظور بهبود قابلیت اطمینان و افزایش اثربخشی برنامه ریزی تعمیرات در مجتمع پتروشیمی شازند انجام شده است..نخست با استفاده از تکنیک داده کاوی بحرانی ترین خوشه از میان خوشه های تشکیل شده توسط نرم افزار مربوطه مشخص می شود، سپس با بهره گیری از رویکرد دیمتل فازی مجموعه ای از بحرانی ترین و تاثیر گزار ترین تجهیزات در خوشه بحرانی، تحت شرایط فازی شناسایی میشوند، در نهایت توسط تکنیک های FMEA وFTA عدد ریسک و علل اصلی خرابی شناسایی خواهد شد و راهکار های مریوطه برای حل مشکلات و بهبود سیستم پیشنهاد خواهد شد.

چکیده انگلیسی:

Taking attention to reliability and maintenance management conception has substantially significantly increased over the recent decades. This paper proposes and integration of DATA MINING, Fuzzy DEMATEL, and FMEA technique in order to improve the reliability and effectiveness of maintenance management in Shazand Petrochemical Company. Firstly, the most critical cluster is selected among final clusters obtained from software by utilizing DATA MINING technique. Secondly, Fuzzy DEMATEL technique is used to identify a collection of most critical and most effective equipment of critical cluster under fuzzy condition. Finally, the FMEA and FTA techniques applied to identify the risks numbers and the main causes of the failure and then the solution will be proposed to solve the problems and improve the system.

منابع و مأخذ:


  1. Alizadeh, S., and Malek Mohammadi, S. (2012). Data mining & knowledge discovery. Khajeh Nasir Toosi University of Technology Publication. Tehran.
    2.
  2. Barforoush, N., Karbasian, M., Molaverdi, N. (2013). The use of CMMS and FMEA for maintenance support for reliability (RCM), Homa Journal, 40.
    3.
  3. Carretero, J., et al. (2003). Applying RCM in large scale systems: A case study with railway networks", Reliability Engineering and System Safety, 82, 257-273.
    4.
  4. Dekker, R., and van Rijn, C. (1996). PROMPT, a decision support system for opportunity-based preventive maintenance. In Reliability and Maintenance of Complex Systems (pp. 530-549). Springer, Berlin, Heidelberg.
    5.
  5. Fontela, E., and Gabus, A. (1976). The DEMATEL observer, DEMATEL 1976 Report, Battelle Geneva Research Center, Switzerland, Geneva.
    6.
  6. Haj-shirmohammadi, A. (2011). Maintenance planning, Esfahan, Ghazal Publishing Press.
    7.
  7. John, B., and Bowles, R.D. (1995). Bonnell, Fuzzy Logic Priorization of Failure in a System Failure mode and effects Criticality analysis, Reliability Engineering and System Safety, 50, 203-213.
    8.
  8. Berry, M.J.A. and Tanoff, G. (1997). Data Mining Technologies, New York: Jon Willey & Sons.
    9.
  9. Sankar, N.R. and Prabhu, B.S. (2011). Modified approach for prioritization of failures in a system failure mode and e_ects analysis, Int J Qual Reliab Manage, 18(3), 324-35.
    10.
  10. Sekhavati, A., and Norouzi, H. (2013). Application of error tree analysis in a gas pressure booster unit, Journal of Management of Oil and Gas Production Supervision.
    11.
  11. Tahmasebi, F., and Shahidi, M. (2015). Data mining of identified risks using the FMEA technique in the insurance industry, First Scientific Conference on New Findings of Management Science, Entrepreneurship and Education, Tehran, Association for the Development and Promotion of Basic Sciences and Technology.
    12.
  12. Worsham, W. C., & Senior Consultant, R. C. I. (2000). Is preventive maintenance necessary?. Maintenance Resources On-Line Magazine, 2225-0492.
    13.
  13. Zafari, M., Nourbakhsh, Z., and SHahba, S. (2012). Evaluation and risk management in steel section production plants using FMEA and Data mining techniques, 2nd Conference on Environmental Planning and Management, Tehran, University of Tehran.
    14.
_||_

  1. Alizadeh, S., and Malek Mohammadi, S. (2012). Data mining & knowledge discovery. Khajeh Nasir Toosi University of Technology Publication. Tehran.
    2.
  2. Barforoush, N., Karbasian, M., Molaverdi, N. (2013). The use of CMMS and FMEA for maintenance support for reliability (RCM), Homa Journal, 40.
    3.
  3. Carretero, J., et al. (2003). Applying RCM in large scale systems: A case study with railway networks", Reliability Engineering and System Safety, 82, 257-273.
    4.
  4. Dekker, R., and van Rijn, C. (1996). PROMPT, a decision support system for opportunity-based preventive maintenance. In Reliability and Maintenance of Complex Systems (pp. 530-549). Springer, Berlin, Heidelberg.
    5.
  5. Fontela, E., and Gabus, A. (1976). The DEMATEL observer, DEMATEL 1976 Report, Battelle Geneva Research Center, Switzerland, Geneva.
    6.
  6. Haj-shirmohammadi, A. (2011). Maintenance planning, Esfahan, Ghazal Publishing Press.
    7.
  7. John, B., and Bowles, R.D. (1995). Bonnell, Fuzzy Logic Priorization of Failure in a System Failure mode and effects Criticality analysis, Reliability Engineering and System Safety, 50, 203-213.
    8.
  8. Berry, M.J.A. and Tanoff, G. (1997). Data Mining Technologies, New York: Jon Willey & Sons.
    9.
  9. Sankar, N.R. and Prabhu, B.S. (2011). Modified approach for prioritization of failures in a system failure mode and e_ects analysis, Int J Qual Reliab Manage, 18(3), 324-35.
    10.
  10. Sekhavati, A., and Norouzi, H. (2013). Application of error tree analysis in a gas pressure booster unit, Journal of Management of Oil and Gas Production Supervision.
    11.
  11. Tahmasebi, F., and Shahidi, M. (2015). Data mining of identified risks using the FMEA technique in the insurance industry, First Scientific Conference on New Findings of Management Science, Entrepreneurship and Education, Tehran, Association for the Development and Promotion of Basic Sciences and Technology.
    12.
  12. Worsham, W. C., & Senior Consultant, R. C. I. (2000). Is preventive maintenance necessary?. Maintenance Resources On-Line Magazine, 2225-0492.
    13.
  13. Zafari, M., Nourbakhsh, Z., and SHahba, S. (2012). Evaluation and risk management in steel section production plants using FMEA and Data mining techniques, 2nd Conference on Environmental Planning and Management, Tehran, University of Tehran.
    14.

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