مروری برکاربرد الگوریتم‌های بهینه‌سازی در پایش وضعیت ماشین‌آلات دوار
محورهای موضوعی : یافته های نوین کاربردی و محاسباتی در سیستم های مکانیکی
مهدی شکارزاده
1
,
بهنام گازرزاده
2
1 - گروه مهندسی مکانیک، دانشگاه آزاد اسلامی، واحد اهواز، اهواز، ایران.
2 - گروه مهندسی مکانیک، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران
کلید واژه: ماشین‌آلات دوار, پایش وضعیت, الگوریتم‌های بهینه‌سازی, تشخیص خرابی,
چکیده مقاله :
پایش وضعیت ماشین‌های دوار به دلیل نقش مهم آن در افزایش قابلیت اطمینان، کاهش خرابی‌های ناگهانی و صرفه‌جویی در هزینه‌های نگهداری، توجه بسیاری را به خود جلب کرده است. روش‌های سنتی پایش وضعیت با چالش‌هایی مانند حجم بالای داده‌ها، وجود نویز و پیچیدگی در تشخیص خرابی روبه‌رو هستند. در سال‌های اخیر، روش‌های بهینه‌سازی به عنوان ابزاری کارآمد برای غلبه بر این مشکلات معرفی شده‌اند و در زمینه‌هایی چون پیش‌پردازش داده‌ها، انتخاب ویژگی و دسته‌بندی خرابی‌ها به کار گرفته می‌شوند. در این مقاله، مروری جامع بر کاربرد الگوریتم‌های بهینه‌سازی در پایش وضعیت ماشین‌های دوار ارایه شده و نقش آن‌ها در بهبود تحلیل ارتعاشات، پایش حرارتی و نگهداری پیش‌بینانه بررسی گردیده است. همچنین ادغام این الگوریتم‌ها با هوش مصنوعی و یادگیری عمیق، چالش‌های محاسباتی و نیاز به تصمیم‌گیری در زمان واقعی پرداخته می‌شود. سپس توصیه‌هایی برای پژوهش‌های آینده با تاکید بر استفاده از روش‌های ترکیبی و پیشرفته ارایه می‌گردد.
Due to the critical role of condition monitoring of rotating machinery in enhancing reliability, reducing unexpected failures, and saving maintenance costs, it has garnered significant attention. Traditional condition monitoring methods face challenges such as large data volumes, noise presence, and complexity in fault diagnosis. In recent years, optimization methods have been introduced as effective tools to overcome these problems and have been applied in areas such as data preprocessing, feature selection, and fault classification. This paper provides a comprehensive review of the applications of optimization algorithms in the condition monitoring of rotating machinery and examines their role in improving vibration analysis, thermal monitoring, and predictive maintenance. Furthermore, the integration of these algorithms with artificial intelligence and deep learning, computational challenges, and the need for real-time decision-making are discussed. Finally, recommendations for future research are presented, emphasizing the use of hybrid and advanced methods.
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