کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6955335 | 1451858 | 2016 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings
ترجمه فارسی عنوان
ساخت شبکه تشخیص سلسله مراتبی بر اساس یادگیری عمیق و کاربرد آن در تشخیص الگوی خطای بلبرینگ اجزای نورد
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
A novel hierarchical diagnosis network (HDN) is proposed by collecting deep belief networks (DBNs) by layer for the hierarchical identification of mechanical system. The deeper layer in HDN presents a more detailed classification of the result generated from the last layer to provide representative features for different tasks. A two-layer HDN is designed for a two-stage diagnosis with the wavelet packet energy feature. The first layer is intended to identify fault types, while the second layer is developed to further recognize fault severity ranking from the result of the first layer. To confirm the effectiveness of HDN, two similar networks constructed by support vector machine and back propagation neuron networks (BPNN) are employed to present a comprehensive comparison. The experimental results show that HDN is highly reliable for precise multi-stage diagnosis and can overcome the overlapping problem caused by noise and other disturbances.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volumes 72â73, May 2016, Pages 92-104
Journal: Mechanical Systems and Signal Processing - Volumes 72â73, May 2016, Pages 92-104
نویسندگان
Meng Gan, Cong Wang, Chang׳an Zhu,