کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7121817 | 1461470 | 2018 | 35 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A fault diagnosis method for roller bearing based on empirical wavelet transform decomposition with adaptive empirical mode segmentation
ترجمه فارسی عنوان
یک روش تشخیص خطا برای غلتک بر اساس تجزیه تبدیل موجک تجربی با تقسیم حالت تجربی سازگار
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
تشخیص گسل، تبدیل موجک تجربی، تقسیم هیستوگرام مقیاس فضا، غلتک،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
چکیده انگلیسی
This paper proposes a fault diagnosis method for roller bearings based on the decomposition of vibration signals using the empirical wavelet transform (EWT) with adaptive empirical mode segmentation and the merging of redundant empirical modes. The proposed method employs scale-space histogram segmentation to determine the boundaries of the empirical modes adaptively, which helps to eliminate the effect of noise and obtain meaningful empirical modes that are more reflective of fault characteristics. In addition, the method merges similar empirical modes to rectify the tendency of conventional EWT to overly decompose empirical modes for fault feature extraction. To this end, an effective merging algorithm based on Pearson's correlation coefficient is developed to divide the empirical modes into groups according to their similarity prior to merging, which avoids a large increase in the amplitude of the signal after merging, and ensures the accuracy of the final result. The performance of the proposed method is first tested using an analytically derived signal. Then, the method is tested using actual vibration signals of roller bearings collected by NASA. The results demonstrate that the proposed method can identify fault information effectively and accurately.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 117, March 2018, Pages 266-276
Journal: Measurement - Volume 117, March 2018, Pages 266-276
نویسندگان
Yueheng Song, Shengkui Zeng, Jiming Ma, Jianbin Guo,