عیب‌یابی سازه جکتی فراساحلی با استفاده از پاسخ‌‌های دینامیکی بر پایه مدل شبیه‌سازی‌شده، حالت سالم مدل واقعی و شبکه عصبی خود رمزنگار عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشگاه تبریز

چکیده

ازآنجایی‌که هزینه نگهداری و تعمیر سیستم‌های مکانیکی اعم از سازه‌ها و ماشین‌های دوار بسیار بالاست، یکی از راه‌های کاهش این هزینه‌ها، درنظرگرفتن تدابیری جهت بررسی عیوب در این سیستم‌ها قبل از هر نوع کار عملی است. در این مطالعه روش جدیدی برای عیب‌یابی سازه جکتی فراساحلی در حضور عدم‌ قطعیت‌های مختلف مانند خطاهای مدل‌سازی، خطاهای اندازه‌گیری و نویزهای محیطی بر پایه مدل شبیه‌سازی‌شده و حالت سالم مدل واقعی ارائه‌شده است. در روش پیشنهادی، داده‌های سازه سالم واقعی برای به‌روزرسانی پارامترهای مدل شبیه‌سازی‌شده استفاده شده‌اند. برخی از بخش‌های سیگنال‌ که مربوط به ذات سیستم نیستند، با استفاده از روش تجزیه مود تجربی دسته‌ای کامل حذف شده‌اند. داده‌های فرکانسی با استفاده از روش تجزیه حوزه فرکانس از سیگنال‌های ارتعاشی استخراج شده‌اند. یک شبکه عصبی خود رمزنگار عمیق، به‌منظور یادگیری ویژگی‌های حساس به عیب از داده‌های فرکانسی و عیب‌یابی سازه طراحی‌شده است. به‌منظور آموزش شبکه عمیق پیشنهادی، از داده‌های فرکانسی مدل شبیه‌سازی‌شده و حالت سالم مدل واقعی استفاده شده‌است. پس‌ازآن، داده‌های فرکانسی سازه واقعی برای ارزیابی شبکه عمیق پیشنهادی استفاده شده‌است. نتایج حاصله نشان می‌دهد که استفاده از روش پیشنهادی برای عیب‌یابی سازه جکتی فراساحلی صحت بالاتری نسبت به سایر روش‌های مقایسه‌ای دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Damage detection of offshore jacket structure using dynamic responses based on simulated model, intact state of real model and deep auto-encoder neural network

نویسندگان [English]

  • zohreh mousavi
  • sina varahram
  • Mir Mohammad Ettefagh
  • Morteza Sadeghi
  • seyed naser razavi
university of tabriz
چکیده [English]

Since the maintenance and repairing costs of mechanical systems, such as structures and rotating machines are significantly high, one way to reduce these costs is to consider some approaches before any operational work to check for damages in such systems. In this study, a new method is presented for damage detection of offshore jacket structures in the presence of various uncertainties, such as modeling errors, measurement errors and environmental noises, based on the simulated model and intact state of the real model. In the proposed method, real intact structure data is used to update the simulated model parameters. Some parts of the signal that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition method. Frequency data is extracted from the vibrational signals using the frequency domain decomposition method. A deep auto-encoder neural network is designed to learn the damage-sensitive features from the frequency data and to damage detection of the structure. In order to train the proposed deep network, frequency data of the simulated model and real intact state are used; then the frequency data of the real structure is used to test the proposed deep network. The results show that the proposed method is capable for damage detection of the offshore jacket structure with more accurate results than the other comparative methods.

کلیدواژه‌ها [English]

  • Condition monitoring
  • Offshore Jacket Structure
  • model updating
  • Deep Neural Network
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