Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings

反褶积 盲反褶积 计算机科学 稳健性(进化) 维纳反褶积 算法 噪音(视频) 人工智能 生物化学 基因 图像(数学) 化学
作者
Yonghao Miao,Ming Zhao,Jing Lin,Xiaoqiang Xu
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:27 (10): 105004-105004 被引量:81
标识
DOI:10.1088/0957-0233/27/10/105004
摘要

De-noising and enhancement of the weak fault signature from the noisy signal are crucial for fault diagnosis, as features are often very weak and masked by the background noise. Deconvolution methods have a significant advantage in counteracting the influence of the transmission path and enhancing the fault impulses. However, the performance of traditional deconvolution methods is greatly affected by some limitations, which restrict the application range. Therefore, this paper proposes a new deconvolution method, named sparse maximum harmonics-noise-ratio deconvolution (SMHD), that employs a novel index, the harmonics-to-noise ratio (HNR), to be the objective function for iteratively choosing the optimum filter coefficients to maximize HNR. SMHD is designed to enhance latent periodic impulse faults from heavy noise signals by calculating the HNR to estimate the period. A sparse factor is utilized to further suppress the noise and improve the signal-to-noise ratio of the filtered signal in every iteration step. In addition, the updating process of the sparse threshold value and the period guarantees the robustness of SMHD. On this basis, the new method not only overcomes the limitations associated with traditional deconvolution methods, minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD), but visual inspection is also better, even if the fault period is not provided in advance. Moreover, the efficiency of the proposed method is verified by simulations and bearing data from different test rigs. The results show that the proposed method is effective in the detection of various bearing faults compared with the original MED and MCKD.
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
岁月如歌发布了新的文献求助10
刚刚
bin完成签到,获得积分10
2秒前
2秒前
很好完成签到,获得积分10
3秒前
小郝已读博完成签到 ,获得积分10
3秒前
3秒前
Self-made完成签到,获得积分10
3秒前
4秒前
5秒前
6秒前
岁月如歌完成签到,获得积分10
7秒前
8秒前
joey完成签到,获得积分10
9秒前
wei完成签到 ,获得积分10
10秒前
miaojuly发布了新的文献求助10
11秒前
11秒前
研友_LXjjOZ发布了新的文献求助150
11秒前
12秒前
13秒前
14秒前
坚定馒头完成签到,获得积分10
14秒前
15秒前
15秒前
星星应助科研通管家采纳,获得30
17秒前
y924758705完成签到,获得积分20
17秒前
打打应助科研通管家采纳,获得10
17秒前
坦率的匪应助科研通管家采纳,获得10
17秒前
烟花应助科研通管家采纳,获得10
17秒前
上官若男应助科研通管家采纳,获得10
17秒前
隐形曼青应助科研通管家采纳,获得10
17秒前
NexusExplorer应助科研通管家采纳,获得10
18秒前
18秒前
YamDaamCaa应助科研通管家采纳,获得30
18秒前
18秒前
18秒前
怡然乌应助科研通管家采纳,获得10
18秒前
18秒前
曹恺悦发布了新的文献求助30
19秒前
20秒前
20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 3988868
求助须知:如何正确求助?哪些是违规求助? 3531255
关于积分的说明 11253071
捐赠科研通 3269858
什么是DOI,文献DOI怎么找? 1804822
邀请新用户注册赠送积分活动 881994
科研通“疑难数据库(出版商)”最低求助积分说明 809035