残余物
判别式
小波
加权
断层(地质)
计算机科学
小波包分解
人工智能
特征(语言学)
模式识别(心理学)
集合(抽象数据类型)
网络数据包
特征提取
数据挖掘
算法
小波变换
地质学
哲学
放射科
医学
地震学
语言学
程序设计语言
计算机网络
作者
Minghang Zhao,Myeongsu Kang,Baoping Tang,Michael Pecht
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2018-05-01
卷期号:65 (5): 4290-4300
被引量:275
标识
DOI:10.1109/tie.2017.2762639
摘要
One of the significant tasks in data-driven fault diagnosis methods is to configure a good feature set involving statistical parameters. However, statistical parameters are often incapable of representing the dynamic behavior of planetary gearboxes under variable operating conditions. Although the use of deep learning algorithms to find a good set of features for fault diagnosis has somewhat improved diagnostic performance, the lack of domain knowledge incorporated into deep learning algorithms has limited further improvement. Accordingly, this paper developed a variant of deep residual networks (DRNs), the so-called deep residual networks with dynamically weighted wavelet coefficients (DRN+DWWC) to improve diagnostic performance, which takes a series of sets of wavelet packet coefficients on various frequency bands as an input. Further, the fact that no general consensus has been reached as to which frequency band contains the most intrinsic information about a planetary gearbox's health status calls for “dynamic weighting layers” in the DRN+DWWC and the role of the layers is to dynamically adjust a weight applied to each set of wavelet packet coefficients to find a discriminative set of features that will be further used for planetary gearbox fault diagnosis.
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