基于动态加权的多尺度残差网络旋转机械故障诊断算法

史红梅,郑畅畅,司瑾,陈晶城

振动与冲击 ›› 2022, Vol. 41 ›› Issue (23) : 67-74.

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (23) : 67-74.
论文

基于动态加权的多尺度残差网络旋转机械故障诊断算法

  • 史红梅,郑畅畅,司瑾,陈晶城
作者信息 +

Fault diagnosis algorithm of rotating machinery based on dynamic weighted multiscale residual network

  • SHI Hongmei, ZHENG Changchang, SI Jin, CHEN Jingcheng
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摘要

针对传统的机械故障诊断方法特征提取困难问题,提出一种新颖的基于特征通道重标定的动态加权多尺度残差网络旋转机械故障诊断方法。首先,将原始数据作为网络的输入,设计宽卷积层进行信息初步融合扩大模型的感受野;再分别构建三个独立的以残差块为基础的并行分支网络,通过设计多尺度卷积核分别从并行分支网络提取深度特征;接着设计动态加权层建立全局信息建模特征通道之间的动态非线性关系,对每个尺度的特征通道进行重标定,提高网络对故障信息的敏感性;最后,将三个尺度的特征进行特征融合,通过分类器实现故障诊断。在多个数据集上进行实验,验证了本文所提算法的有效性。
关键词:旋转机械故障诊断;动态加权;一维残差网络;多尺度学习

Abstract

Aiming at the difficulty of feature extraction in traditional mechanical fault diagnosis methods, a novel dynamic weighted multi-scale residual network rotating machine fault diagnosis method based on feature channel recalibration is proposed. Firstly, the raw data is taken as the input of the network, and a wide convolution layer is designed to preliminarily fuse the information and expand the receptive field of the model; Then three independent parallel branch networks based on residual blocks are constructed, and the depth features are extracted from the parallel branch networks by designing multi-scale convolution kernels; Next, a dynamic weighting layer is designed to model the dynamic nonlinear relationship between feature channels using global information, and recalibrate the feature channels of each scale to improve the sensitivity of the network to fault information. Finally, the features of the three scales are fused, and the fault diagnosis is realized by the classifier. Experiments on several datasets verify the effectiveness of the proposed algorithm.
Key words: Fault diagnosis of rotating machinery; Dynamic weighting; One-dimensional residual network; Multi-scale learning

关键词

旋转机械故障诊断 / 动态加权 / 一维残差网络 / 多尺度学习

Key words

Fault diagnosis of rotating machinery / Dynamic weighting / One-dimensional residual network / Multi-scale learning

引用本文

导出引用
史红梅,郑畅畅,司瑾,陈晶城. 基于动态加权的多尺度残差网络旋转机械故障诊断算法[J]. 振动与冲击, 2022, 41(23): 67-74
SHI Hongmei, ZHENG Changchang, SI Jin, CHEN Jingcheng. Fault diagnosis algorithm of rotating machinery based on dynamic weighted multiscale residual network[J]. Journal of Vibration and Shock, 2022, 41(23): 67-74

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