A wavelet-based despiking algorithm for large data of structural health monitoring
A wavelet-based despiking algorithm for large data of structural health monitoring
复制标题
基于小波的大数据结构健康监测去尖峰算法
DOI：
10.1177/1550147718819095
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发表时间：
2018-12
影响因子：
2.3
通讯作者：
Yi-Qing Ni
中科院分区：
文献类型：
--
作者：
Yun-Xia Xia;Yi-Qing Ni
近二十年来，土木结构长期健康监测技术的应用迅速增加。结构健康监测系统收集了大量的现场数据。然而，从大型数据库中挖掘与结构状态相关的信息仍然是一个巨大的挑战。在结构健康监测信号中，尖峰信号是常见的异常现象，其振幅较大，可能表现出复杂的空间和时间模式。它们可能会在基于数据的结构状态评估中引入重大错误。特别是长期的结构健康监测数据量特别大。为了消除其中的尖峰，该算法是高度期望的自动和高效的。本文以小波变换为理论基础，提出了一种无监督的快速削峰方法。该方法分为尖峰信号检测和尖峰信号去除两个步骤。该方法将假设检验和小波模极大值链搜索算法结合到棘波检测过程中。因此，可以快速识别尖峰的到达时间。然后，利用基于最大重叠离散小波变换的跨尺度极大极小搜索算法去除尖峰信号，保留不受影响的信息。在棘波检测步骤中不需要进行逆变换，提高了算法的速度。尖峰消除算法仅从尖峰出现的频带中去除尖峰;因此，去除尖峰后未受影响的信号分量是完整的。通过对一座真实的桥梁的3组结构健康监测数据进行分析，并与时域法、频域滤波法和传统小波法进行比较。
The last two decades have witnessed a rapid increase in the applications of long-term structural health monitoring technologies to the civil structures. A wealth of field data has been collected by the structural health monitoring systems. Nevertheless, the mining of information associated with structural condition from the large database is still a great challenge. In the structural health monitoring signals, spikes are commonly encountered anomalies that have large amplitudes and may show complex spatial and temporal patterns. They can introduce significant errors in data-based condition assessment of structures. Particularly, the long-term structural health monitoring data have an extraordinarily large volume. To remove the spikes in it, the algorithm is highly desired to be both automatic and efficient. An unsupervised and fast despiking method is proposed in this article on the theoretical cornerstone of the wavelet transform. This method is implemented by two steps, namely, spike detection and spike removal. The hypothesis testing and algorithm of searching wavelet modulus maxima chain are incorporated into the spike-detection procedure. Thus, the arrival time of the spikes can be identified fast. And then, the spikes are removed by a cross-scale maxima and minima search algorithm based on the maximum overlap discrete wavelet transform, retaining the unaffected information. The inverse transformation is not required in the spike-detection step, which improves the speed of the algorithm. The spike-removal algorithm removes spikes only from their occurrence frequency bands; thus, the unaffected signal components are intact after despiking. The proposed algorithm is demonstrated using three sets of structural health monitoring data collected from a real bridge, comparing with three other approaches, that is, the time-domain method, frequency filter and traditional wavelet method.
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DOI：
10.1201/9781420048254.ch5
发表时间：
1998-12
期刊：
--
影响因子：
--
作者：
C. Schwarz;J. Welsh
通讯作者：
C. Schwarz;J. Welsh
影响因子：
2.2
作者：
Wen-Yu He;Songye Zhu
通讯作者：
Wen-Yu He;Songye Zhu
DOI：
10.1109/18.119748
发表时间：
1992-03
期刊：
IEEE Trans. Inf. Theory
影响因子：
--
作者：
M. Frisch;H. Messer
通讯作者：
M. Frisch;H. Messer
影响因子：
4.6
作者：
Nenadic, Z;Burdick, JW
通讯作者：
Burdick, JW
DOI：
10.1063/1.4823127
发表时间：
1992-05
期刊：
Computers in Physics
影响因子：
--
作者：
I. Daubechies
通讯作者：
I. Daubechies