پیش‌بینی مدت‌زمان تعمیر تجهیز پمپ اصلی روان‌کاری با استفاده از منطق فازی و شبکه عصبی- فازی و به‌دست آوردن دسترس‌پذیری و شاخص‌های آن با استفاده از مدل شبیه‌سازی مونت کارلو در سیستم‌های تولید توان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده منابع طبیعی و محیط زیست، واحد علوم تحقیقات،دانشگاه آزاد اسلامی، تهران، ایران

2 گروه مهندسی سیستم‌های انرژی، دانشگده مکانیک، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

3 دانشکده منابع طبیعی و محیط زیست، واحد علوم تحقیقات،دانشگاه آزاد اسلامی، تهران، ایران /گروه فنی و مهندسی واحد آستارا، دانشگاه آزاد اسلامی، آستارا، ایران

چکیده

خرابی و تعمیر تجهیزات، نقش تعیین‌کننده‌ای در دسترس‌پذیری کل سیستم دارد. در پژوهش حاضر، به ارائه یک راهکار کاربردی جهت تحلیل زمان تعمیر تجهیزات و پیش‌بینی رفتار تجهیز پرداخته شده است. جهت تخمین زمان خرابی و مدت‌زمان تعمیر تجهیزات، از تجربه فرد متخصص استفاده گردیده است؛ لذا این پژوهش، برروی تخمین زمان تعمیر و نرخ تعمیر تجهیز پمپ اصلی روان‌کاری در سیستم تولید توان توربین گازی با رویکرد وارد نمودن تجربه انسانی تمرکز نموده است. در مرحله بعد، یک تحلیل پیش‌بینی دسترس‌پذیری سالیانه تجهیز در یک بازه زمانی 20 ساله انجام گرفته که بدین ترتیب، سال‌های بحرانی تجهیز از نظر مدت‌زمان خرابی با ارزیابی و بررسی دسترس‌پذیری سالیانه مشخص می‌شود. برای این هدف، با استفاده از منطق فازی، از یک پایگاه دانش و تجربه انسانی جهت برآورد مدت‌زما‌‌ن‌های تعمیر استفاده شده و با طراحی یک سیستم عصبی-فازی، کل زما‌‌ن‌های تعمیر شبیه‌سازی شده است؛ که جهت تخمین و پیش‌بینی زمان تعمیر تجهیز به‌کار برده شده است. در ادامه، با استفاده از روش شبیه‌سازی مونت کارلو، دسترس‌پذیری سالیانه، نرخ تعمیر وابسته به‌زمان و سایر شاخص‌های دسترس‌پذیری محاسبه شده است. مدل هدف، پمپ اصلی سیستم روغن‌کاری واحد توربین گازی پالایشگاه آبادان در ایران است. بررسی نتایج به‌دست‌آمده، نشان می‌دهد که اعمال تعمیرات پیش‌گیرانه در بازه‌های زمانی بهینه 150 تا 160 روزه، تأثیر به‌سزایی در افزایش دسترس‌پذیری تجهیز داشته و منجر به کاهش بازرسی‌های دوره‌ای اضافی می‌گردد. همچنین حداقل دسترس‌پذیری سیستم، 96% و حداکثر 99% پیش‌بینی شده است.
 

کلیدواژه‌ها


عنوان مقاله [English]

Main Oil Pump Equipment Repair Time Prediction with Fuzzy logic and Adaptive neuro Fuzzy System and Availability assessment and their Related Indices with Monte Carlo Simulation in Power Generation Systems

نویسندگان [English]

  • Danesh Mirzaey 1
  • Ali Behbahaninia 2
  • Ashkan Abdalisousan 3
  • Sayed Mohammadreza Miri Lavasani 1
1 Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Energy Systems Engineering, Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
3 Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran\Department of Engineering and Technology, Astara Branch, Islamic Azad University, Astara, Iran
چکیده [English]

Equipment failure, repairs and maintenance play a decisive role in the availability of the entire system. This study presents a practical solution for analyzing equipment repairs and maintenance time and predicting equipment behavior. Expert experience has been used to estimate failure time and equipment repair time; therefore, this study has focused on estimating the repair time and repair rate of equipping the main lubricating oil pump in the gas turbine power generation system with the approach of entering human experience (HE). In the next step, an analysis of the Equipment›s Annual Availability Forecast is performed over a period of 20 years, thus, the critical years of the equipment are determined in terms of downtime by evaluating and reviewing the annual availability. For this purpose, a database of human knowledge and experience has been simulated to estimate the repair times used using fuzzy logic, and the whole process of repair times has been simulated by designing a neural-fuzzy system; which is used to estimate and predict equipment repair time. Then, the annual availability, time-dependent repair rate and other availability indicators are calculated using the Monte Carlo simulation method. The target model is the main lubricating oil pump system of the gas turbine unit of Abadan refinery in Iran. According to the results, applying preventive repairs at optimal intervals of 150 to 160 days, has a significant effect on increasing the availability of equipment and leads to a reduction in additional periodic inspections. Also, the minimum and the maximum system availability is predicted to be 96% and 99%, respectively.

کلیدواژه‌ها [English]

  • Uzzy Logic
  • Adaptive Neural Fuzzy System
  • Membership Function
  • Availability
  • Reliability
  • Repair Rate
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