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首页> 外文期刊>Mechanical systems and signal processing >Passive sensing method for impact localisation in composite plates under simulated environmental and operational conditions
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Passive sensing method for impact localisation in composite plates under simulated environmental and operational conditions

机译：模拟环境和运营条件下复合板中冲击定位的被动传感方法

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A novel feature extraction method is developed for impact localisation based on Artificial Neural Networks (ANNs) in sensorized Composite structures subjected to environmental and operational conditions. Impact induced lamb waves are investigated for the first time for different impact scenarios (angle, mass and energy) on flat and curved plates under environmental (temperature range) and operational (vibration) conditions. The Time of Arrival (TOA) is significantly influenced by these conditions hence complicating the impact localisation. To overcome this complication, a novel and robust TOA extraction method is proposed. It is based on Normalised Smoothed Envelope Threshold (NSET) coupled with a high pass filter to remove vibration noise prior to TOA extraction. Localisation ANNs were trained with data from a single baseline impact condition and were tested under impacts with varying conditions. It was shown that by using the proposed method for TOA extraction, the trained ANN is able to better predict the location of impacts compared to an ANN trained with data from common TOA extraction methods (detection area 0.99-56.08% of sensing region versus 0.28-1.55% for NSET). The developed method gives consistent accuracy and significantly reduces the required training data, making ANN based impact localisation more feasible for real life application. (C) 2019 Elsevier Ltd. All rights reserved.
机译：一种新颖的特征提取方法是基于人工神经网络（ANNS）的影响定位，经受环境和操作条件的传感复合结构。在环境（温度范围）下，在平面和弯曲板上的不同影响场景（角度，质量和能量）和操作（振动）条件下，对冲击诱导的λ波浪进行研究。到达时间（TOA）受这些条件的显着影响，因此使影响本地化复杂化。为了克服这种并发症，提出了一种新颖和强大的TOA提取方法。它基于归一化平滑的外壳阈值（NSET），其与高通滤波器耦合，以在TOA提取之前去除振动噪声。本地化ANNS从单个基线冲击条件的数据接受培训，并在不同条件下的影响下进行测试。结果表明，通过使用来自共同TOA提取方法的数据（检测区域0.98-56.08％的数据，培训的ANN能够更好地预测受训练的ANN的影响的位置1.55％的nset）。开发方法提供一致的准确性，并显着降低所需的培训数据，使基于ANN的影响本地化更加可行的真实寿命应用。 （c）2019 Elsevier Ltd.保留所有权利。

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