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Damage localisation in composite and metallic structures using a structural neural system and simulated acoustic emissions

机译：使用结构神经系统和模拟声发射，对复合材料和金属结构中的损伤进行定位

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Detecting and locating damage in structural components and joints that have high feature densities and complex geometry is a difficult problem in the field of structural health monitoring (SHM). Active propagation of diagnostic waves is one approach that is used to detect damage. But small cracks and damage are difficult to detect because they have a small effect on the propagating waves as compared to the effects the complex geometry itself which causes dispersion and reflection of waves. Another limitation of active wave propagation is that pre-damage data is required for every sensor-actuator combination, and a large number of sensors might be needed to detect small cracks on large structures. Overall, the problem of detecting damage in complex geometries is not well investigated in the field of SHM. Nevertheless, the problem is important because damage often initiates at joints and locations where section properties change. Recently there have been advances in the development of a passive structural neural system (SNS) for damage detection. The SNS uses electronic logic circuits to mimic the signal processing in the biological neural system. The advantage of the SNS is that highly distributed continuous sensors provide high sensitivity to damage, and the biomimetic signal processing and passive sensing tremendously simplify the instrumentation and wiring of the monitoring system. Also, the SNS operates continuously during operation of the structure to detect ambient Lamb waves or bulk waves that are produced by cracking, delamination, bearing damage, rotor imbalance, flow instabilities, impacts, or other material failure modes. In this paper, asymmetric Lamb wave propagation representing acoustic emissions (AE) is modelled based on a superposition of plate bending vibration modes. The simulation demonstrates that the SNS with four channels of data acquisition can localize damage within a grid of sensors irrespective of the number of sensors in the network. To experimentally validate the analysis results, a two-neuron prototype of the SNS was built and tested using a simulated AE source (a pencil lead break) on a riveted aluminium joint and on a composite plate. In both experiments, the SNS was able to localize simulated damages. These results indicate the feasibility of expanding the SNS to a large number of neurons.
机译：在具有高特征密度和复杂几何形状的结构部件和接头中，检测和定位损坏是结构健康监测（SHM）领域中的难题。诊断波的主动传播是一种用于检测损坏的方法。但是，由于与传播波的复杂性和几何形状本身的影响相比，它们对传播波的影响较小，因此很难检测到小裂纹和损坏。有源波传播的另一个局限性是，每个传感器-执行器组合都需要预损伤数据，并且可能需要大量传感器来检测大型结构上的小裂缝。总体而言，在SHM领域中，对复杂几何形状中的损坏进行检测的问题尚未得到充分研究。但是，此问题很重要，因为损坏通常是在截面属性改变的接缝和位置处引发的。最近，用于损伤检测的被动结构神经系统（SNS）的开发取得了进展。 SNS使用电子逻辑电路来模仿生物神经系统中的信号处理。 SNS的优点是高度分布的连续传感器对损坏具有很高的敏感性，仿生信号处理和无源传感极大地简化了监视系统的仪器和接线。同样，SNS在结构运行期间连续运行，以检测由裂纹，分层，轴承损坏，转子不平衡，流动不稳定性，撞击或其他材料故障模式产生的环境Lamb波或体波。在本文中，基于板弯曲振动模式的叠加对代表声发射（AE）的非对称Lamb波传播进行了建模。仿真表明，具有四个数据采集通道的SNS可以在传感器网格内定位损坏，而与网络中的传感器数量无关。为了通过实验验证分析结果，在铆接铝制接头和复合板上使用模拟AE源（铅笔引线断裂）构建并测试了SNS的两神经元原型。在两个实验中，SNS都能定位模拟的损坏。这些结果表明将SNS扩展到大量神经元的可行性。

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