کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7152684 1462419 2015 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
ترجمه فارسی عنوان
استفاده از تجزیه حالت تجربی و شبکه عصبی مصنوعی برای تشخیص گسل اتوماتیک تحمل بر اساس سیگنال های ارتعاش
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی مکانیک
چکیده انگلیسی
Condition monitoring and fault diagnosis of rolling element bearings (REBs) are at present very important to ensure the steadiness of industrial and domestic machinery. According to the non-stationary and non-linear characteristics of REB vibration signals, feature extraction method is based on empirical mode decomposition (EMD) energy entropy in this paper. A mathematical analysis to select the most significant intrinsic mode functions (IMFs) is presented. Therefore, the chosen features are used to train an artificial neural network (ANN) to classify bearings defects. Experimental results indicated that the proposed method based on run-to-failure vibration signals can reliably categorize bearing defects. Using a proposed health index (HI), REB degradations are perfectly detected with different defect types and severities. Experimental results consist in continuously evaluating the condition of the monitored bearing and thereby detect online the severity of the defect successfully. This paper shows potential application of ANN as effective tool for automatic bearing performance degradation assessment without human intervention.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Acoustics - Volume 89, March 2015, Pages 16-27
نویسندگان
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