A nonlinearity-sensitive approach for detection of “breathing” cracks relying on energy modulation effect

声学 调制(音乐) 非线性系统 能量(信号处理) 呼吸 物理 材料科学 机械 生物系统 计算机科学 结构工程 控制理论(社会学) 统计 数学 工程类 人工智能 生物 麻醉 医学 量子力学 控制(管理)
作者
Maosen Cao,Qitian Lu,Zhongqing Su,Maciej Radzieński,Wei Xu,Wiesław Ostachowicz
出处
期刊:Journal of Sound and Vibration [Elsevier BV]
卷期号:524: 116754-116754 被引量:21
标识
DOI:10.1016/j.jsv.2022.116754
摘要

• We propose a nonlinearity-sensitive approach for the detection of “breathing” cracks. • The energy modulation effect with its physical sense is reported. • The quadratic Teager-Kaiser energy can enhance hidden higher harmonics. • The approach is experimentally validated by non-contact laser measurement. For a cracked structural component under a single-tone harmonic excitation, the opening-closing motion of the “breathing” crack can lead to higher harmonics in its steady-state responses, which can be efficient indicators for the detection of the crack. Nevertheless, when the opening-closing motion of a “breathing” crack is slight, higher harmonics can become barely visible in frequency spectra and seem to be hidden. As a consequence, the crack can hardly be detected by such hidden higher harmonics. Addressing this problem, this study proposes a nonlinearity-sensitive approach for the detection of “breathing” cracks. In particular, a novel phenomenon of energy modulation effect (EME) is reported, based on which a new concept of quadratic Teager-Kaiser energy (Q-TKE) is formulated. Hidden higher harmonics can be considerably enhanced in Q-TKEs, such that “breathing” cracks can be readily detected. A physical insight into the mechanism of the EME is provided. The approach is numerically verified using the finite element method and experimentally validated through non-contact laser measurement. The results suggest that hidden higher harmonics can be considerably enhanced in the Q-TKEs and become sensitive indicators to manifest the occurrence of the cracks, suitable for the detection of initial fatigue cracks.
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
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
woshi123应助认真的TOTORO采纳,获得10
刚刚
刚刚
lulumomoxixi完成签到 ,获得积分10
刚刚
1秒前
2秒前
2秒前
2秒前
3秒前
沉默涵梅发布了新的文献求助10
5秒前
情怀应助鸡腿子采纳,获得10
6秒前
微笑芯完成签到,获得积分10
6秒前
7秒前
7秒前
爆米花应助徐1采纳,获得10
7秒前
阿饭发布了新的文献求助10
7秒前
可靠的火车完成签到,获得积分10
8秒前
HIT_C完成签到,获得积分10
8秒前
英姑应助有魅力的蘑菇采纳,获得10
9秒前
9秒前
木今完成签到,获得积分10
9秒前
袁气奶豆发布了新的文献求助20
10秒前
10秒前
11秒前
11秒前
12秒前
小苏同学完成签到,获得积分10
12秒前
12秒前
13秒前
榶七七发布了新的文献求助10
13秒前
Trace2023完成签到,获得积分10
13秒前
AAA完成签到,获得积分10
13秒前
研友_惊鸿发布了新的文献求助10
13秒前
123完成签到,获得积分10
13秒前
14秒前
14秒前
wwl完成签到,获得积分10
15秒前
乐乐应助mayamaya采纳,获得10
16秒前
sagitar应助木今采纳,获得20
17秒前
单薄凌蝶发布了新的文献求助10
17秒前
靓丽的白昼完成签到,获得积分10
17秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7581681
求助须知:如何正确求助?哪些是违规求助? 9160736
关于积分的说明 19600318
捐赠科研通 7163870
什么是DOI,文献DOI怎么找? 3266005
关于科研通互助平台的介绍 2430943
邀请新用户注册赠送积分活动 2257096