کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6753465 | 1430812 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Development of a morphological convolution operator for bearing fault detection
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی عمران و سازه
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Sound and Vibration - Volume 421, 12 May 2018, Pages 220-233
Journal: Journal of Sound and Vibration - Volume 421, 12 May 2018, Pages 220-233
نویسندگان
Yifan Li, Xihui Liang, Weiwei Liu, Yan Wang,