نوع مقاله : علمی پژوهشی

نویسندگان

1 استادیار، گروه مهندسی صنایع، 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 به عنوان یکی از گزاره‌های اصلی نگهداری و تعمیرات پیشگویانه، نقش ویژه‌ای به منظور 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، با توجه 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 پیش-بینی 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. Graduate, 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 unique 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 traditional 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 findings of this study can be used to predict the remaining useful life of aviation industry equipment and to provide appropriate maintenance programs.

کلیدواژه‌ها [English]

  • Aviation industry
  • Deterioration prediction
  • Deep learning
  • Turbofan engine
  • Supervised learning
  • Artificial neural networks
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