涡流脉冲热像技术中基于神经网络的检出/漏检预测研究

Prediction of Hit/Miss under Different Detection Conditions through Eddy Current Pulsed Thermography

涡流脉冲热像技术是一种新型的无损检测技术,已在金属材料和复合材料的检测领域得到了广泛应用。检出/漏检则是评价被检测对象是否存在裂纹的重要标准,为解决目前检出/漏检研究需要大量实验数据的问题,本文提出了一种基于BP神经网络的检出/漏检预测方法。首先,制作了30组含有不同尺寸疲劳裂纹的金属试件,并完成了15组不同检测条件下的裂纹检测实验。其次,分别绘制了3组检出概率曲线,并完成了不同检测条件对检出概率的影响分析。最后,为实现检出/漏检的可靠性预测,构建了基于BP神经网络的检出/漏检预测模型,并以50组数据为样本进行测试,实现了不同检测条件下不同尺寸裂纹的检出/漏检0误差预测。

Eddy current pulsed thermography is an emerging nondestructive testing technique that has been widely used for flaw detection in metallic materials.Typically,its performance is evaluated through hit/miss analysis.However,the traditional method of analyzing hit/miss requires considerable experimental data,which is time-consuming and expensive.In this study,a model-assisted method based on back-propagation neural networks(BPNNs)for hit/miss prediction was developed to minimize the need for additional experimental tests.Thirty sets of metal specimens with fatigue cracks of different lengths were fabricated;15 experimental groups were subjected to different detection conditions.Subsequently,three sets of the probability of detection(POD)curves were plotted,and the effects of the different detection conditions on the POD were analyzed.Finally,a prediction model of the hit/miss based on the BPNN was constructed,and the hit/miss prediction was realized.The results showed that under different detection conditions,the proposed framework could complete the hit/miss prediction with an error of zero.