GUIDED WAVE HEALTH MONITORING OF COMPLEX STRUCTURES
GUIDED WAVE HEALTH MONITORING OF COMPLEX STRUCTURES
复制标题
复杂结构的导波健康监测
DOI：
10.25560/5288
复制
发表时间：
2009
影响因子：
9.1
通讯作者：
T. Clarke
中科院分区：
文献类型：
--
作者：
T. Clarke
结构健康监测（SHM）系统被广泛认为能够显著降低诸如航空航天、核以及石油和天然气等行业中的安全关键结构的检查成本。成功的SHM系统可以被认为是那些联合收割机了对缺陷的良好灵敏度，优选地具有定位和识别能力，以及低传感器密度的系统。基于稀疏阵列传感器的技术，产生和接收导波是最有前途的候选人之一。导波在大的距离上传播，并且某些模式具有通过各种结构特征传输的能力，从而导致相对少量的分布式传感器能够覆盖该结构。在包含高密度结构元素的复杂结构中，由于大量的重叠反射，所获得的时间轨迹通常太复杂而不能直接解释。在这种情况下，基线减影技术变得很有吸引力。在该方法中，从在结构的操作的初始阶段期间已经获取的信号中减去来自结构的电流信号。这消除了对复杂原始时间信号进行解释的需要，并且只要在结构未损坏时减去基线信号后获得的残余信号的幅度足够低，任何缺陷都将被清楚地看到。然而，众所周知，诸如应力、环境温度变化和液体负载的环境效应会影响导波的速度;如果使用在不同条件下获取的单个基线，则这会修改时间轨迹并导致高水平的残留信号。在这些影响中，温度变化是最常见的，并且是关键的，因为它们不仅影响波的传播，而且影响换能器的响应。本工作的目的是展示大面积复杂结构的导波健康监测的潜力。它开始与大面积结构的检查和监测的一般文献综述，在该技术的优点和缺点相比，其他成熟的SHM技术2。本文介绍了两种不同的温度稳定型换能器的设计和性能，它们能在低于200 kHz的频率范围内产生高A0或S 0模纯度。评估了不同的信号处理技术旨在减少或消除温度对波传播的影响的效率，并提出了一种温度补偿信号处理策略。最后，一个大型的金属结构是用来证明一个稀疏阵列SHM系统的基础上，这种信号处理策略，和成像算法被用来联合收割机的信息从大量的传感器组合，最终导致人为引入的结构中的缺陷的定位。
Structural Health Monitoring (SHM) systems are widely regarded as capable of significantly reducing inspection costs of safety-critical structures in industries such as aerospace, nuclear, and oil and gas, among others. Successful SHM systems can be considered those which combine good sensitivity to defects, preferably with the capability of localization and identification, with a low sensor density. Techniques based on sparse arrays of sensors which generate and receive guided waves are among the most promising candidates. Guided waves propagate over large distances and certain modes have the ability to transmit through a variety of structural features leading to a relatively small number of distributed sensors being able to cover the structure. In complex structures, which contain high densities of structural elements, the timetraces obtained are often too complex to be directly interpreted due to the large number of overlapping reflections. In this case, the Baseline Subtraction technique becomes attractive. In this method a current signal from the structure is subtracted from a signal which has been acquired during the initial stages of operation of the structure. This eliminates the need for interpretation of the complex raw time signal and any defects will be clearly seen provided the amplitude of the residual signal obtained after subtraction of the baseline signal is sufficiently low when the structure is undamaged. However, it is well known that environmental effects such as stress, ambient temperature variations and liquid loading affect the velocity of guided waves; this modifies the time-traces and leads to high levels of residual signal if a single baseline, taken under different conditions, is used. Of these effects, temperature variations are the most commonly encountered and are critical since they affect not only the wave propagation but also the response of transducers. The present work aims to demonstrate the potential of guided wave health monitoring of large area complex structures. It starts with a general literature review on inspection and monitoring of large area structures, in which the advantages and disadvantages of this technique compared to other well-established SHM techniques 2 are presented. The design and behaviour of two different temperature-stable transducers generating high A0 or S0 mode purity in the sub-200kHz frequency region are described. The efficiency of different signal processing techniques aimed at reducing or eliminating the influence of temperature on wave propagation is evaluated and a temperature compensation signal processing strategy is proposed. Finally, a large metallic structure is used to demonstrate a sparse-array SHM system based on this signal processing strategy, and imaging algorithms are used to combine the information from a large number of sensor combinations, ultimately leading to the localization of defects artificially introduced in the structure.
DOI：
10.1098/rspa.2007.1834
发表时间：
2007-06
期刊：
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
影响因子：
--
作者：
K. Worden;C. Farrar;G. Manson;G. Park
通讯作者：
K. Worden;C. Farrar;G. Manson;G. Park
影响因子：
4.3
作者：
Konstantinidis, Georgios;Wilcox, Paul D.;Drinkwater, Bruce W.
通讯作者：
Drinkwater, Bruce W.
DOI：
10.1109/sas13374.2008.4472945
发表时间：
2008-03
期刊：
2008 IEEE Sensors Applications Symposium
影响因子：
--
作者：
J. Michaels;A. Croxford;P. Wilcox
通讯作者：
J. Michaels;A. Croxford;P. Wilcox