基于多尺度动态加权多级残差卷积自编码的旋转机械信号降噪方法

doi:10.3901/JME.2024.18.053

机械工程学报 ›› 2024, Vol. 60 ›› Issue (18): 53-63.doi: 10.3901/JME.2024.18.053

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基于多尺度动态加权多级残差卷积自编码的旋转机械信号降噪方法

杜文辽^1,2, 杨凌凯^1,2, 王宏超^1,2, 巩晓赟^1,2, 赵峰^1,2, 李川^1,2

1. 郑州轻工业大学河南省机械装备智能制造重点实验室郑州 450002;
2. 郑州轻工业大学机电工程学院郑州 450002

收稿日期:2023-09-21 修回日期:2024-03-20 出版日期:2024-09-20 发布日期:2024-11-15
作者简介:杜文辽,男,1977年出生,博士,教授,博士研究生导师。主要研究方向为机械故障诊断和智能计算。E-mail:dwenliao@zzuli.edu.cn
李川,男,1975年出生,博士,教授,博士研究生导师。主要研究方向为机械故障诊断和智能计算。E-mail:chuanli@21cn.com
王宏超(通信作者),男,1983年出生,博士,讲师。主要研究方向为机械故障诊断和智能计算。E-mail:2015072@zzuli.edu.cn
基金资助:
国家自然科学基金面上(52275138,12202405)、河南省重点研发专项(231111221100)、河南省重大科技专项(221100220200)、河南省重点实验室开放基金(KL03C2104)和盐城市重点研发计划竞争(BE2023024)资助项目。

Multiscale Dynamic Weighted and Multilevel Residual Convolution Autoencoder Based Rotating Mechanical Signals Denoising

DU Wenliao^1,2, YANG Lingkai^1,2, WANG Hongchao^1,2, GONG Xiaoyun^1,2, ZHAO Feng^1,2, LI Chuan^1,2

1. Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002;
2. College Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002

Received:2023-09-21 Revised:2024-03-20 Online:2024-09-20 Published:2024-11-15

摘要/Abstract

摘要： 振动信号常用于监测旋转机械的运行状态，但在采集过程中往往包含大量噪声，为了尽可能去除噪声干扰，提出一种噪声学习的新型神经网络，即基于多尺度动态加权多级残差卷积自编码网络(MDW-MRSCAE)的降噪方法。确切地，通过深度卷积自编码和残差模块结合，使自编码网络对噪声成分进行编码和解码，通过学习噪声特性取代传统学习干净信号。为了解决网络学习中梯度消失问题，提高收敛速度，构建多层残差结构。特别的，在网络中加入多尺度特征提取网络层加强网络对噪声的特征提取能力。为体现不同卷积核的全局贡献，构造动态加权网络层，自适应调整各卷积核的全局权重，进一步提高网络去噪能力。通过典型模拟信号和不同故障实际轴承信号，验证网络去噪有效性。结果表明，该网络模型优于已发表的同类最新模型，信号高频噪声得到明显抑制。

关键词: 噪声学习, 多尺度卷积核, 动态加权, 多级残差网络, 信号去噪

Abstract: Vibration signals are used to monitor the running state of rotating machinery, but always suffer from a lot of noise. In order to eliminate noise interference as much as possible, a new noise-learning based neural network was proposed, namely, a multiscale dynamic weight and multilevel residual convolution autoencoder (MDW-MRSCAE) based signals denoising model. In specifically, through the combination of deep convolutional autoencoder and residual module, the autoencoder network can encode and decode the noise components, and replace the traditional learning clean signal by learning the noise characteristics. In order to solve the problem of disappearing gradient in network learning and improve the convergence speed, a multilevel residual structure is constructed. In particular, the multiscale feature extraction network layer is added to enhance the feature extraction ability of the network. In order to reflect the global contribution of different convolution kernels, a dynamic weighted network layer is constructed to adaptively adjust the global weight of each convolution kernel, and further improve the denoising ability of the network. The effectiveness of network denoising was verified by typical analog signals and actual bearing signals with different faults. The results show that the network model is superior to the latest published models of the same kind, and the high frequency noise of the signal is suppressed obviously.

Key words: noise learning, multiscale convolution kernel, dynamic weight, multilevel residual, signal denoising

中图分类号:

TH133
TH165

杜文辽, 杨凌凯, 王宏超, 巩晓赟, 赵峰, 李川. 基于多尺度动态加权多级残差卷积自编码的旋转机械信号降噪方法[J]. 机械工程学报, 2024, 60(18): 53-63.

DU Wenliao, YANG Lingkai, WANG Hongchao, GONG Xiaoyun, ZHAO Feng, LI Chuan. Multiscale Dynamic Weighted and Multilevel Residual Convolution Autoencoder Based Rotating Mechanical Signals Denoising[J]. Journal of Mechanical Engineering, 2024, 60(18): 53-63.

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基于多尺度动态加权多级残差卷积自编码的旋转机械信号降噪方法

Multiscale Dynamic Weighted and Multilevel Residual Convolution Autoencoder Based Rotating Mechanical Signals Denoising

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