Terahertz Nondestructive Testing Signal Recognition Based on PSO-BP Neural Network

Meihui JIA; Lijuan LI; Jiaojiao REN; Jian GU; Dandan ZHANG; Jiyang ZHANG; Weihua XIONG

doi:10.3788/gzxb20215009.0930004

Acta Photonica Sinica, Volume. 50, Issue 9, 0930004(2021)

Terahertz Nondestructive Testing Signal Recognition Based on PSO-BP Neural Network

Meihui JIA... Lijuan LI, Jiaojiao REN, Jian GU, Dandan ZHANG, Jiyang ZHANG and Weihua XIONG |Show fewer author(s)

Key Laboratory of Optoelectronic Measurement and Control and Optical Information Transmission Technology of Ministry of Education， College of Optoelectronic Engineering， Changchun University of Science and Technology， Changchun130022， China

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Figures & Tables(13)

Fig. 1. Defect sample drawing of high temperature resistant composite material with multi-adhesive structure

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Fig. 2. Terahertz time domain waveforms of different defect areas

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Fig. 3. Operating principle diagram of BP neural network

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Fig. 4. Flow chart of optimization process

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Fig. 5. Schematic diagram of high temperature resistant composite bonding sample

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Fig. 6. Schematic diagram of reflective THZ-TDS

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Fig. 7. Comparison of mean square errors of training results between BP neural network and PSO-BP neural network.

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Fig. 8. Defect recognition results of BP neural network.

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Fig. 9. Defect recognition results of PSO-BP neural network

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Fig. 10. Recognition results of different degree of debonding defects by PSO-BP neural network

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Fig. 11. Error comparison of PSO-BP neural network in recognizing defects of different degrees

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Table 1. Time domain characteristics and their expressions

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Table 1. Time domain characteristics and their expressions

Serial number	Characteristics of the name	Characteristic expression
1	Kurtosis	$K_{m i n} = \frac{E [(X_{i} {- \sum_{i = 1}^{n} X_{i} / n)}^{4}]}{{E [(X_{i} {- \sum_{i = 1}^{n} X_{i} / n)}^{2} {]}}^{2}}$
2	Skewness	$S_{m i n} = \frac{E [(X_{i} {- \sum_{i = 1}^{n} X_{i} / n)}^{3}]}{{E [(X_{i} {- \sum_{i = 1}^{n} X_{i} / n)}^{2} {]}}^{3 / 2}}$
3	Waveform factor	$X_{p p - u p} = \frac{\sqrt[]{\sum_{i = 1}^{n} {X_{i}}^{2} / n}}{\sum_{i = 1}^{n} \|X_{i}\| / n}$
4	Absolute mean of amplitude	${\|X\|}_{m e a n} = \sum_{i = 1}^{n} \|X_{i}\| / n$
5	In peak value	$X_{p v} = X_{m a} - X_{m i}$
6	Minimum amplitude value	$X_{i m i n}$

Table 2. Defects of the sample
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Table 2. Defects of the sample
The serial number Upper defect level Lower defect level
1 150 μm 350 μm
2 100 μm No defect
3 No defect 250 μm
4 No defect 100 μm

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Meihui JIA, Lijuan LI, Jiaojiao REN, Jian GU, Dandan ZHANG, Jiyang ZHANG, Weihua XIONG. Terahertz Nondestructive Testing Signal Recognition Based on PSO-BP Neural Network[J]. Acta Photonica Sinica, 2021, 50(9): 0930004

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