Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures

被引:443
作者
Amezquita-Sanchez, Juan Pablo [1 ]
Adeli, Hojjat [2 ]
机构
[1] Univ Autonomous Queretaro, Fac Engn, San Juan Del Rio 76807, Queretaro, Mexico
[2] Ohio State Univ, Dept Civil Environm & Geodet Engn, Columbus, OH 43220 USA
关键词
EMPIRICAL MODE DECOMPOSITION; BLIND SOURCE SEPARATION; WAVELET NEURAL-NETWORK; TRUSS-TYPE STRUCTURE; TIME-FREQUENCY REPRESENTATION; RESOLUTION SPECTRAL-ANALYSIS; DAMAGE DETECTION; PARAMETER-IDENTIFICATION; CRACK DETECTION; NONLINEAR IDENTIFICATION;
D O I
10.1007/s11831-014-9135-7
中图分类号
TP39 [计算机的应用];
学科分类号
080201 [机械制造及其自动化];
摘要
Signal processing is the key component of any vibration-based structural health monitoring (SHM). The goal of signal processing is to extract subtle changes in the vibration signals in order to detect, locate and quantify the damage and its severity in the structure. This paper presents a state-of-the-art review of recent articles on signal processing techniques for vibration-based SHM. The focus is on civil structures including buildings and bridges. The paper also presents new signal processing techniques proposed in the past few years as potential candidates for future SHM research. The biggest challenge in realization of health monitoring of large real-life structures is automated detection of damage out of the huge amount of very noisy data collected from dozens of sensors on a daily, weekly, and monthly basis. The new methodologies for on-line SHM should handle noisy data effectively, and be accurate, scalable, portable, and efficient computationally.
引用
收藏
页码:1 / 15
页数:15
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