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首页> 外文期刊>Mechanical systems and signal processing >Traceability of Acoustic Emission measurements for a proposed calibration method - Classification of characteristics and identification using signal analysis
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Traceability of Acoustic Emission measurements for a proposed calibration method - Classification of characteristics and identification using signal analysis

机译:提出的校准方法的声发射测量的可追溯性-特征分类和信号分析识别

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摘要

When using Acoustic Emission (AE) technologies, tensile, compressive and shear stress/ strain tests can provide a detector for material deformation and dislocations. In this paper improvements are made to standardise calibration techniques for AE against known metrics such as force. AE signatures were evaluated from various calibration energy sources based on the energy from the first harmonic (dominant energy band). The effects of AE against its calibration identity are investigated: where signals are correlated to the average energy and distance of the detected phenomena. In addition, extra tests are investigated in terms of the tensile tests and single grit tests characterising different materials. Necessary translations to the time-frequency domain were necessary when segregating salient features between different material properties. Continuing this work the obtained AE is summarised and evaluated by a Neural Network (NN) regression classification technique which identifies how far the malformation has progressed (in terms of energy/force) during material transformation. Both genetic-fuzzy clustering and tree rule based classifier techniques were used as the second and third classification techniques respectively to verify the NN output giving a weighted three classifier system. The work discussed in this paper looks at both distance and force relationships for various prolonged Acoustic Emission stresses. Later such analysis was realised with different classifier models and finally implemented into the Simulink simulations. Further investigations were made into classifier models for different material interactions in terms of force and distance which add further dimension to this work with different materials based simulation realisations. Within the statistical analysis section there are two varying prolonged stress tests which together offer the mechanical calibration system (automated solenoid and pencil break calibration system). Taking such a mechanical system with the real-time simulations gives a fully automated accurate AE calibration system to force and distance measurement phenomena.
机译:使用声发射(AE)技术时,拉伸,压缩和剪切应力/应变测试可以为材料变形和位错提供检测器。在本文中,进行了改进以针对已知度量(例如力)对AE的校准技术进行标准化。根据来自一次谐波(主能带)的能量,从各种校准能源中评估AE签名。研究了AE对其校准身份的影响:其中,信号与检测到的现象的平均能量和距离相关。此外,还针对表征不同材料的拉伸试验和单粒度试验研究了额外的试验。当在不同材料特性之间隔离显着特征时,必须将时频转换为必要的。继续这项工作,将通过神经网络(NN)回归分类技术对获得的AE进行汇总和评估,该技术可识别材料转化过程中畸形进展的程度(以能量/力计)。遗传-模糊聚类和基于树规则的分类器技术分别用作第二和第三分类技术,以验证给出加权三分类器系统的神经网络输出。本文讨论的工作着眼于各种长时间的声发射应力的距离和力关系。后来,使用不同的分类器模型实现了此类分析,并最终将其实施到Simulink仿真中。在力和距离方面针对不同材料相互作用的分类器模型进行了进一步研究,这为基于不同材料的模拟实现为这项工作增加了新的维度。在统计分析部分中,有两种不同的延长应力测试,它们一起提供机械校准系统(自动螺线管和铅笔折断校准系统)。将这样的机械系统与实时仿真结合使用,可以提供一个全自动的精确AE校准系统,以进行力和距离测量现象。

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