计算机科学
过度拟合
卷积神经网络
人工智能
卷积(计算机科学)
连续小波变换
模式识别(心理学)
断层(地质)
特征提取
局部二进制模式
方位(导航)
人工神经网络
二进制数
二元分类
小波
离散小波变换
小波变换
支持向量机
数学
直方图
算术
图像(数学)
地质学
地震学
作者
Yiwei Cheng,Manxi Lin,Jun Wu,Haiping Zhu,Xinyu Shao
标识
DOI:10.1016/j.knosys.2021.106796
摘要
This paper presents a data-driven intelligent fault diagnosis approach for rotating machinery (RM) based on a novel continuous wavelet transform-local binary convolutional neural network (CWT-LBCNN) model. The proposed approach builds an end-to-end diagnosis mechanism, and does not need manual feature extraction. By feeding the inputting vibration signal, features are captured adaptively and fault condition of RM is diagnosed automatically. Different from traditional CNNs, the proposed CWT-LBCNN utilizes a local binary convolution layer to replace a traditional convolution layer, which enables CWT-LBCNN to have faster training speed and less proneness to overfitting. Two experimental studies including bearing fault diagnosis and gearbox compound fault diagnosis show that the proposed CWT-LBCNN has more stable and reliable prediction accuracy than other existing methods.
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