Early fault diagnosis of ball screws based on 1-D convolution neural network and orthogonal design

滚珠丝杠 卷积神经网络 球(数学) 计算机科学 人工神经网络 特征提取 人工智能 润滑 卷积(计算机科学) 断层(地质) 模式识别(心理学) 工程类 机械工程 数学 几何学 地质学 地震学 螺母
作者
Chen Yin,Yulin Wang,Yan He,Lu Liu,Yan Wang,Guannan Yue
出处
期刊:Proceedings Of The Institution Of Mechanical Engineers, Part O: Journal Of Risk And Reliability [SAGE Publishing]
卷期号:235 (5): 783-797 被引量:6
标识
DOI:10.1177/1748006x21992886
摘要

Ball screws, the most frequently used mechanical components to transform rotary motion into linear motion, can directly affect the precision and service life of engineering machines. Once the efficiency and accuracy of ball screws degrades, the performance and safety of machines are hard to guarantee. Conventional fault diagnosis researches of ball screws are mainly focused on ordinary faults such as preload loss and wear, and lack of the researches on early faults such as lubrication degradation which may progress into the ordinary faults. Additionally, the fault diagnosis models proposed in previous studies divide the fault diagnosis into two separated stages: feature extraction and fault classification, which prevents the usage for real-time applications. The specifically designed algorithm in features extraction stage may be also not workable on other objects. To tackle these drawbacks, this paper proposes a highly accurate early fault diagnosis model of ball screws based on a state-of-the-art deep learning technique, called One-Dimensional Convolutional Neural Network (1-D CNN). Experiments simulating the lubrication degradation of ball screws are specially designed for the early fault diagnosis of the ball screws. Moreover, a concise and efficient approach based on orthogonal design is exploited to scientifically obtain the optimal parameters of the 1-D CNN. The results of a case study verify the superiority of the proposed method in establishing a highly accurate 1-D CNN based fault diagnosis model.
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
keyun发布了新的文献求助10
刚刚
刚刚
jason0023完成签到,获得积分10
刚刚
失眠的狗发布了新的文献求助10
刚刚
孝铮完成签到 ,获得积分10
1秒前
执着的松鼠完成签到,获得积分20
1秒前
nicole关注了科研通微信公众号
1秒前
1秒前
2秒前
山楂完成签到,获得积分10
2秒前
3秒前
希望天下0贩的0应助王某采纳,获得10
3秒前
NPC-CBI完成签到,获得积分10
4秒前
wonder123发布了新的文献求助30
5秒前
vv完成签到,获得积分10
5秒前
5秒前
5秒前
畅快芾完成签到,获得积分10
5秒前
6秒前
Mo发布了新的文献求助50
7秒前
phoenix完成签到,获得积分10
7秒前
ding应助干净又晴采纳,获得10
8秒前
虚拟的怀绿完成签到,获得积分10
8秒前
krisliu完成签到,获得积分10
8秒前
舍予有服发布了新的文献求助10
10秒前
酷波er应助老迟到的梦旋采纳,获得10
10秒前
10秒前
畅快芾发布了新的文献求助10
11秒前
小周完成签到,获得积分10
13秒前
田様应助jiajia采纳,获得10
14秒前
Hello应助zjs采纳,获得10
15秒前
ttong发布了新的文献求助10
15秒前
15秒前
ablesic.rong完成签到,获得积分10
16秒前
sml发布了新的文献求助10
16秒前
cutey小鲸鱼完成签到,获得积分20
17秒前
you完成签到,获得积分10
18秒前
krisliu发布了新的文献求助10
19秒前
YJ888发布了新的文献求助10
20秒前
干净又晴发布了新的文献求助10
21秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3989550
求助须知:如何正确求助?哪些是违规求助? 3531774
关于积分的说明 11254747
捐赠科研通 3270278
什么是DOI,文献DOI怎么找? 1804966
邀请新用户注册赠送积分活动 882125
科研通“疑难数据库(出版商)”最低求助积分说明 809176