Terahertz Imaging-based Quantitative Characterization of SSA-CNN for Internal Delamination Defects in Layered Composite Insulation Structures

doi:10.11985/2023.03.007

Abstract

Abstract:

The geometry and location of delamination defects inside a laminated composite insulating structure can cause field distortion during operation, leading to partial discharges and even insulation breakdown, so it is important to accurately detect the degree of delamination inside a laminated composite insulating part. Firstly, the terahertz spectroscopy is used to image the laminated composite insulation structure with delamination defects in frequency domain and a typical image set of delamination defects is obtained. Based on this, the DCGAN model is used to expand the images and a data set is built to achieve sample expansion and equalization. Finally, the geometric area of defects in the delamination region of the defect samples is calculated and analyzed by three SSA-CNN(semantic self-attentive) models. The results show that the DeepLabV3+(MobileNetV2) model has the highest pixel accuracy and the recognition rate of the layered region can reach 97.59%, and the geometric size of the defects in the layered region can be successfully characterized by the calculation of pixel points. The results of the study can provide a technical reference for the non-contact quantitative characterization of delamination defects inside laminated composite insulation structures.

Key words: Terahertz NDT, damped defect identification, SSA-CNN, DeepLabV3+, MobileNetV2

CLC Number:

TM561

DUO Wenbo, LI Hongwei, LI Shuaibing, YANG Dong, LU Baopeng, KANG Yongqiang, CAO Binglei. Terahertz Imaging-based Quantitative Characterization of SSA-CNN for Internal Delamination Defects in Layered Composite Insulation Structures[J]. Journal of Electrical Engineering, 2023, 18(3): 63-72.

Figures/Tables 14

References 30

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试验环境	数值
环境温度/℃	25
环境湿度(%)	30
光谱扫描精度/ps	0.04
扫描宽度/mm	50
扫描长度/mm	50
扫描步长	0.08

试验阶段	参数	数值
冻结阶段	批处理尺寸大小	4
冻结阶段	迭代次数	100
解冻阶段	批处理尺寸大小	2
	学习率	余弦退火法
	学习率上限	5×10^-4
	学习率下限	5×10^-6
	迭代次数	150

迭代次数	训练损失 (Trian loss)	验证损失 (Val loss)	平均交并比 (MIoU)
1	0.692	0.657	0
40	0.137	0.379	93.747
50	0.127	0.175	94.259
90	0.082	0.146	93.633
100	0.084	0.154	93.717
140	0.057	0.062	94.761
150	0.043	0.058	94.820
190	0.039	0.050	95.192
200	0.037	0.052	95.098
240	0.036	0.048	94.937
250	0.038	0.049	94.919

网络模型	召回率(%)	精确度(%)	平均交并比(%)
PSPNet(MobileNetV2)	97.12	97.06	94.38
DeepLabV3+(Xception)	97.34	97.57	94.92
DeepLabV3+ (MobileNetV2)	97.55	97.59	95.28