فناوری در مهندسی هوافضا

فناوری در مهندسی هوافضا

طبقه‌بندی و شناسایی عیوب موتور توربوجت با شبکه عصبی خودسازمان‌ده و خوشه‌بندی ماشین بردار پشتیبان

نوع مقاله : علمی پژوهشی

نویسندگان
1 کارشناس ارشد، دانشکده مهندسی هوافضا، دانشگاه صنعتی مالک اشتر، تهران
2 دانشیار، دانشکده مهندسی هوافضا، دانشگاه صنعتی مالک اشتر، تهران
3 استادیار، دانشکده مهندسی هوافضا، دانشگاه صنعتی مالک اشتر، تهران
10.22034/jtae.2026.10.2.8
چکیده
توسعه‌ سیستم‌های نظارت بر سلامت موتور یکی از چالش‌های صنعت هوانوردی است که می‌تواند با بهبود قابلیت دسترسی‌پذیری و قابلیت اطمینان موتور، صرفه‌جویی قابل توجهی به همراه آورد. یک ساختار پیش‌بینی سلامت، با ارائه تخمینی دقیق و به موقع از وضعیت سلامت فعلی موتور، زمینه کاهش تعمیرات و نگهداری برنامه‌ریزی‌نشده را فراهم می‌کند. جهت دستیابی به راندمان بالاتر در تشخیص عیوب و نیز تسهیل شروع سریع این روند، نیاز به الگوریتم با حساسیت کمتر نسبت به حجم داده‌های آموزشی در این مسئله وجود دارد. در این مقاله یک الگوریتم تشخیص عیب موتور توربوجت، مبتنی بر شبکه عصبی خودسازمان‌ده ارائه شده است که از خوشه‌بندی داده‌ها برای این هدف استفاده می‌کند. این روش یادگیری، خوشه‌های مستقل داده‌ها را با روش ماشین بردار پشتیبان تعیین نموده و اثربخشی فرآیند آموزش را افزایش می‌دهد. برای شناسایی الگوی عیوب از شبکه عصبی پیشخور دولایه و برای طبقه‌بندی عیوب از تکنیک بردارهای پشتیبان ماشین استفاده شده است. بدین ترتیب، حجم داده‌های آموزشی مورد استفاده جهت راه‌اندازی الگوریتم به شدت کاهش می‌یابد و در نتیجه، هزینه و زمان تست‌های زمینی و پروازی مورد نیاز برای تأمین داده‌ها کمتر می‌شود. نتایج بدست آمده از پیاده‌سازی الگوریتم روی داده‌های شبیه‌‌سازی شده سنسورهای موتور J85 در حالات سالم، خوردگی، سایش، رسوب‌گرفتگی و خطای نازل سوخت، حاصل از یک مدل ترمودینامیکی صحه‌‌سنجی شده، نشان‌دهنده توانایی روش پیشنهادی در پایش سلامت به روش عملکردی است. همچنین به رغم کاهش داده مورد استفاده جهت آموزش شبکه به یک پنجم، دقت شناسایی عیوب از 8/99 به 7/97 کاهش یافته که مطلوب است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Classification And Identification Of Turbojet Engine Faults With Self-Organizing Neural Networks And Support Vector Machine Clustering

نویسندگان English

Farideh Sadat Hashemifard 1
Mehdi Jahromi 2
Mostafa Khazaee 3
Mostafa Mahmoodi 2
1 Faculty of Aerospace, Malek Ashtar University of Technology, Tehran, Iran
2 Faculty of Aerospace, Malek Ashtar University of Technology, Tehran, Iran
3 Faculty of Aerospace, Malek Ashtar University of Technology, Tehran, Iran
چکیده English

The development of engine health monitoring systems is one of the challenges of the aviation industry that can bring significant savings by improving engine availability and reliability. A health prediction framework reduces the unplanned repairs and maintenance through an accurate and timely estimate of the current engine health condition. In order to achieve higher efficiency in fault detection and also facilitate the rapid start of this process, an algorithm is required that be less sensitive to the amount of training data in this problem. This paper proposes a fault detection algorithm for a turbojet engine based on a self-organizing neural network that uses the clustered data. This training method specifies the independent clusters of data through support vector machine (SVM) and makes the training process more efficient. A two-layer feedforward neural network identifies the fault patterns, and an SVM technique classifies them. The results on health, corrosion, erosion, fouling, and fuel nozzle faults from a validated thermodynamic model of the J85 engine show the capability of the proposed method in performance diagnostics. In addition, despite the significant reduction in the training data, the accuracy of fault detection has decreased from 99.8 to 97.7 which is satisfactory.

کلیدواژه‌ها English

Engine health monitoring
Fault diagnosis and prognostics
Fault classification
Neural network
Turbojet engine
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  • تاریخ دریافت 21 اردیبهشت 1404
  • تاریخ بازنگری 18 آذر 1404
  • تاریخ پذیرش 06 دی 1404
  • تاریخ اولین انتشار 08 بهمن 1404