کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6955241 | 1451855 | 2016 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis
ترجمه فارسی عنوان
ساخت چند ضلعی تطبیقی مبتنی بر آنتروپی چندتروپکتیو و کاربرد آن برای تشخیص خطای مکانیکی
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کلمات کلیدی
تشخیص گسل، گسل مرکب، چند وویو متناسب آنتروپی مولتی فرکتال،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
Compound-fault diagnosis of mechanical equipment is still challenging at present because of its complexity, multiplicity and non-stationarity. In this work, an adaptive redundant multiwavelet packet (ARMP) method is proposed for the compound-fault diagnosis. Multiwavelet transform has two or more base functions and many excellent properties, making it suitable for detecting all the features of compound-fault simultaneously. However, on the other hand, the fixed basis function used in multiwavelet transform may decrease the accuracy of fault extraction; what's more, the multi-resolution analysis of multiwavelet transform in low frequency band may also leave out the useful features. Thus, the minimum sum of normalized multifractal entropy is adopted as the optimization criteria for the proposed ARMP method, while the relative energy ratio of the characteristic frequency is utilized as an effective way in automatically selecting the sensitive frequency bands. Then, The ARMP technique combined with Hilbert transform demodulation analysis is then applied to detect the compound-fault of bevel gearbox and planetary gearbox. The results verify that the proposed method can effectively identify and detect the compound-fault of mechanical equipment.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volumes 76â77, August 2016, Pages 742-758
Journal: Mechanical Systems and Signal Processing - Volumes 76â77, August 2016, Pages 742-758
نویسندگان
Shuilong He, Jinglong Chen, Zitong Zhou, Yanyang Zi, Yanxue Wang, Xiaodong Wang,