فناوری در مهندسی هوافضا

فناوری در مهندسی هوافضا

ارائه مدلی جهت پیش‌بینی روند زوال تجهیزات در صنایع هوایی مبتنی بر مفهوم یادگیری عمیق (مطالعه موردی: موتور توربوفن هواپیما)

نوع مقاله : علمی پژوهشی

نویسندگان
1 استادیار، دانشکده فنی و مهندسی، دانشگاه جامع امام حسین (ع)، تهران، ایران
2 کارشناس‌ارشد، دانشکده فنی و مهندسی، دانشگاه جامع امام حسین (ع)، تهران، ایران
چکیده
مدیریت سلامت و پیش­بینی عیوب به‌عنوان یکی از گزاره­های اصلی نگهداری و تعمیرات پیشگویانه، نقش ویژه­ای به‌منظور شناسایی، تشخیص و پیش­بینی وضعیت سلامت انواع دارایی­های فیزیکی ایفا می­نماید. ارزیابی وضعیت سلامت انواع دارایی­ها در صنعت هوایی، با هدف ارائه برنامه‌های نگه‌داشت، از طریق برآورد وضعیت زوال/ تخریب یکی از راهکارهای اساسی است. در این مطالعه، باتوجه به محدودیت­ها و عدم قطعیت­های موجود در روش­‌های مرسوم پیش­بینی در حوزه تعیین روند زوال تجهیزات، یک مدل توسعه­یافته شبکه عصبی مصنوعی با محوریت مفهوم یادگیری عمیق و مقایسه آن با سایر روش­های مرسوم ارائه شد. نتایج مقایسه­ای نشان داد که روش شبکه‌ عصبی پرسپترون عمیق با دقت پیش­بینی 94 درصد دارای عملکرد بالایی در تعیین روند زوال در موتورهای توربینی هواپیما در مقایسه با سایر روش­های مرسوم داشته است. نتایج حاصل از این تحقیق  می­تواند در پیش­بینی عمر مفید باقی­مانده و نیز ارائه برنامه­های مناسب نگه‌داشت در تجهیزات صنایع هوایی مورد استفاده باشد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

A Model for Equipment Deterioration Prediction in Aviation Industry Using Deep Learning Methods (Case Study: Airplane Turbofan Engine)

نویسندگان English

Saeed Ramezani 1
Hamzeh Soltanali 1
Amir Beirami 2
1 Assistant Professor, Faculty of Engineering, University of Iman Hossein (IHU), Tehran, Iran
2 M. Sc., Faculty of Engineering, University of Iman Hossein (IHU), Tehran, Iran
چکیده English

One of the main propositions of predictive maintenance is Prognostics and Health Management (PHM), which plays a special role in identifying, diagnosing, and predicting the health status of physical assets. To that end, one of the fundamental solutions is to assess the condition of the equipment in the aviation sector in order to provide maintenance plans by determining the trend of deterioration or destruction. In this study, a developed model of an artificial neural network was presented, focusing on the concept of deep learning and its comparison with other conventional methods in response to the limitations and uncertainties in conventional prediction methods in determining the deterioration process of the equipment. The comparative results revealed that the deep learning neural network method with a prediction accuracy of 94% had a high performance in determining the deterioration process in aircraft turbine engines compared to other conventional methods. The results of this research can be used to predicting the remaining useful life and also providing appropriate maintenance programs in aviation industry equipment.

کلیدواژه‌ها English

Aviation Industry
Deterioration Prediction
Deep Learning
Turbofan Engine
Supervised Learning
Artificial Neural Networks
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  • تاریخ دریافت 30 خرداد 1402
  • تاریخ بازنگری 24 تیر 1402
  • تاریخ پذیرش 14 مرداد 1402
  • تاریخ اولین انتشار 14 مرداد 1402