Multi-scale attention mechanism residual neural network for fault diagnosis of rolling bearings

残余物 人工智能 计算机科学 核(代数) 断层(地质) 模式识别(心理学) 人工神经网络 特征提取 方位(导航) 块(置换群论) 卷积(计算机科学) 深度学习 振动 算法 地质学 物理 地震学 组合数学 量子力学 数学 几何学
作者
Yan Wang,Jie Liang,Xiaoguang Gu,Dan Ling,Haowen Yu
出处
期刊:Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science [SAGE Publishing]
卷期号:236 (20): 10615-10629 被引量:21
标识
DOI:10.1177/09544062221104598
摘要

Rolling bearing fault diagnosis is crucial to improve industrial safety and reliability. In recent years, intelligent fault diagnosis method represented by deep learning (DL) has been receiving increasing attention. In order to ameliorate the full training of the deep network, improve the model accuracy, and perfect the analysis of mechanical vibration signals with huge amount of information, a multi-scale attention mechanism residual network (MSA-ResNet) fault diagnosis method is proposed in this paper. First, an attention mechanism block is introduced to construct a new type of residual block combination. Second, a multi-scale structure is constructed by choosing an appropriate convolution kernel size. Finally, the overall framework of MSA-ResNet is constructed for efficient training and failure pattern recognition. The MSA-ResNet algorithm introduces an attention mechanism in each residual module of the residual network (ResNet), which improves the sensitivity to features. The features of different scales are obtained through the multi-scale convolution kernel, and the multi-scale feature extraction of complex nonlinear mechanical vibration signals is realized. The processing of original vibration signal rarely involves artificial interference, which is more conducive to industrial application of the proposed method. Diagnostic experiments are conducted on bearing datasets from the Case Western Reserve University (CWRU) and the Machinery Failure Prevention Technology (MFPT) to verify the effectiveness of the proposed method. The results illustrating the rolling bearing fault diagnosis method based on MSA-ResNet have advantages in multi-scale feature extraction, noise immunity, and fault classification accuracy.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Lendar完成签到 ,获得积分10
刚刚
RuiBigHead发布了新的文献求助10
1秒前
2秒前
跳跃的洋葱完成签到 ,获得积分10
2秒前
2秒前
yangjoy完成签到,获得积分10
3秒前
pinklay完成签到 ,获得积分10
3秒前
3秒前
科研通AI5应助ttt采纳,获得10
4秒前
重要问旋完成签到,获得积分10
4秒前
5秒前
香蕉觅云应助科研通管家采纳,获得10
6秒前
6秒前
酷波er应助科研通管家采纳,获得10
6秒前
华仔应助科研通管家采纳,获得10
6秒前
wanci应助科研通管家采纳,获得30
6秒前
老阎应助科研通管家采纳,获得30
6秒前
姜莹应助科研通管家采纳,获得10
6秒前
隐形曼青应助科研通管家采纳,获得10
6秒前
顾矜应助科研通管家采纳,获得10
6秒前
共享精神应助科研通管家采纳,获得10
6秒前
大模型应助科研通管家采纳,获得10
6秒前
在水一方应助科研通管家采纳,获得10
6秒前
英姑应助科研通管家采纳,获得10
6秒前
CodeCraft应助科研通管家采纳,获得10
6秒前
6秒前
ED应助科研通管家采纳,获得10
7秒前
NexusExplorer应助科研通管家采纳,获得10
7秒前
7秒前
思源应助科研通管家采纳,获得10
7秒前
7秒前
orixero应助科研通管家采纳,获得10
7秒前
斯文败类应助科研通管家采纳,获得10
7秒前
香蕉觅云应助科研通管家采纳,获得10
7秒前
斯可完成签到,获得积分10
7秒前
彭于晏应助科研通管家采纳,获得10
7秒前
华仔应助科研通管家采纳,获得10
7秒前
7秒前
7秒前
7秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038619
求助须知:如何正确求助?哪些是违规求助? 3576294
关于积分的说明 11375058
捐赠科研通 3306084
什么是DOI,文献DOI怎么找? 1819374
邀请新用户注册赠送积分活动 892698
科研通“疑难数据库(出版商)”最低求助积分说明 815066