کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7152395 | 1462379 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A new method to classify railway vehicle axle fatigue crack AE signal
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی مکانیک
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
This paper presents a new method for classifying railway vehicle axle fatigue crack acoustic emission (AE) signal. The method is developed by integrating self-adaptive empirical mode decomposition (EMD) with Elman neural network (ENN). The method first uses EMD to decompose the signals into six intrinsic mode functions (IMFs) and one residual. From the IMFs and the residual obtained by EMD, a three-dimensional energy (TDE) feature vector consisting of energy entropy, energy distribution ratio, and interval average energy are computed by Hilbert-Huang transform. An ENN will be trained using the TDE feature vector to classify the AE signal caused by railway vehicle axle fatigue crack. The result shows that this method is better than other EMD energy domain classification method on identifying railway vehicle axle fatigue crack AE signal.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Acoustics - Volume 131, February 2018, Pages 174-185
Journal: Applied Acoustics - Volume 131, February 2018, Pages 174-185
نویسندگان
Yong Zhou, Li Lin, Da Wang, Miao He, David He,