کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6954306 1451829 2018 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings
ترجمه فارسی عنوان
گروه کامل مکانیسم میانگین محلی با نویز تطبیقی ​​و کاربرد آن در تشخیص خطا برای بلبرینگ نورد
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی
Mode mixing resulting from intermittent signals is an annoying problem associated with the local mean decomposition (LMD) method. Based on noise-assisted approach, ensemble local mean decomposition (ELMD) method alleviates the mode mixing issue of LMD to some degree. However, the product functions (PFs) produced by ELMD often contain considerable residual noise, and thus a relatively large number of ensemble trials are required to eliminate the residual noise. Furthermore, since different realizations of Gaussian white noise are added to the original signal, different trials may generate different number of PFs, making it difficult to take ensemble mean. In this paper, a novel method is proposed called complete ensemble local mean decomposition with adaptive noise (CELMDAN) to solve these two problems. The method adds a particular and adaptive noise at every decomposition stage for each trial. Moreover, a unique residue is obtained after separating each PF, and the obtained residue is used as input for the next stage. Two simulated signals are analyzed to illustrate the advantages of CELMDAN in comparison to ELMD and CEEMDAN. To further demonstrate the efficiency of CELMDAN, the method is applied to diagnose faults for rolling bearings in an experimental case and an engineering case. The diagnosis results indicate that CELMDAN can extract more fault characteristic information with less interference than ELMD.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volume 106, June 2018, Pages 24-39
نویسندگان
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