دسته‌بندی شدت عیب اتصال کوتاه بین دور موتورهای سنکرون مغناطیس دائم با استفاده از درخت تصمیم‌گیری و شبکه عصبی بیزین
محورهای موضوعی : مهندسی برق و کامپیوترعباس درویشی 1 , سید محسن سید موسوی 2 * , بهزاد مشیری 3
1 - دانشکده مهندسی برق، دانشگاه آزاد اسلامی، واحد اهواز، اهواز، ایران
2 - دانشکده مهندسی برق، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران،
3 - دانشکده مهندسی برق و کامپیوتر، دانشگاه تهران، تهران، ایران
کلید واژه: اتصال کوتاه بین دور, موتور سنکرون مغناطیس دائم, استخراج ویژگی, انتخاب ویژگی, درخت تصمیم¬گیری, شبکه عصبی بیزین.,
چکیده مقاله :
این مقاله به بررسی شناسایی شدت عیب اتصال کوتاه بین دور در یک موتور سنکرون مغناطیس دائم با توان ۳ کیلووات با استفاده از درخت تصمیم‌گیری و شبکه عصبی عمیق بیزین می‌پردازد. مجموعه داده اصلی شامل سیگنال‌های جریان سه‌فاز در شرایط سالم و دارای عیب بوده که شش سطح از شدت عیب را شامل می‌شود. یک مرحله پیش‌پردازش برای تحلیل داده‌ها در حوزه‌های زمان و فرکانس انجام می‌شود. در این فرآیند از تبدیل موجک گسسته و تحلیل چگالی طیفی توان استفاده شده است. برای کاهش ابعاد فضای آموزشی، ابتدا معیارهای آماری مانند میانگین، انحراف معیار، کشیدگی و چولگی به دست می‌آیند. سپس از تحلیل مولفه‌های اصلی کرنل برای تعیین برجسته‌ترین ویژگی‌ها استفاده می‌شود. الگوریتم درخت تصمیم‌گیری برای شناسایی وضعیت عیب موتور آموزش داده می‌شود. در نهایت،یک شبکه عصبی عمیق مبتنی بر تئوری بیزین برای تشخیص شدت عیب اتصال کوتاه بین دور به کار گرفته می‌شود. عملکرد الگوریتم پیشنهادی از نظر دقت، صحت، بازخوانی و امتیاز 1F با در نظر گرفتن تعداد متفاوتی از ویژگی‌های غالب منتخب ارزیابی می‌شود.
This paper investigates the identification of inter-turn short-circuit fault severity in a 3-kilowatt permanent magnet synchronous motor using a decision tree and a deep Bayesian neural network. The primary dataset includes three-phase current signals under both healthy and faulty conditions, covering six fault severity levels. A preprocessing stage is conducted to analyze the data in time and frequency domains using discrete wavelet transform and power spectral density analysis. To reduce the dimensionality of the feature space, statistical indicators such as mean, standard deviation, kurtosis, and skewness are initially extracted. Kernel principal component analysis is then employed to identify the most salient features. A decision tree algorithm is trained to detect motor fault conditions. Finally, a deep Bayesian neural network is applied to classify the severity of the inter-turn short-circuit fault. The proposed algorithm’s performance is evaluated in terms of accuracy, precision, recall, and F1-score, considering varying numbers of selected dominant features.
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