基于相位和表面温度的缺陷定量识别及其对比研究

陈林; 黎敏谦; 高峰; 刘敏; 张弛

基于相位和表面温度的缺陷定量识别及其对比研究

陈林^1,,
黎敏谦¹,
高峰¹,
刘敏²,
张弛¹

1.
海军士官学校兵器系, 安徽蚌埠 233000
2.
海军92957部队, 浙江舟山 316000

基金项目: “十三五”军队重点院校和重点学科专业建设计划资助

详细信息

作者简介:
陈林（1986-），男，博士，讲师，主要从事红外跟踪识别及检测方面研究。E-mail：chenlinhonghu@163.com

中图分类号: TK38
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- 被引次数: 0
出版历程
- 收稿日期: 2020-11-09
- 修回日期: 2021-02-28
- 刊出日期: 2022-06-20

Quantitative Identification and Comparative Study of Defects Based on Phase and Surface Temperatures

1.
Weaponry Department, Naval Petty Officer Academy, Bengbu 233000, China
2.
Unit 92957 of Navy, Zhoushan 316000, China

摘要

摘要: 为了提高红外检测的精度，实现缺陷深度和大小的同步检测，将共轭梯度反演算法分别与脉冲检测技术和脉冲相位检测技术相结合，实现了基于相位和表面温度的红外定量识别，通过数字算例对比分析了不同因素对识别结果的影响。研究结果表明: 在无测温误差时，基于相位和表面温度的识别都能准确地识别缺陷的位置大小；基于相位和表面温度的识别结果精度都会因随机误差的增大而降低；基于表面温度的识别结果精度会因均匀误差的增大而降低，但是均匀误差对基于相位的识别无影响。
- 定量识别 /
- 对比研究 /
- 红外检测 /
- 共轭梯度
Abstract: To improve the accuracy of infrared detection and realize synchronous detection of defect depth and size, a conjugate gradient recognition algorithm is integrated with pulsed thermography and pulsed phase thermography. Quantitative identification of infrared technology is achieved based on the phase and surface temperatures. The influence of these factors on the identification result is analyzed using numerical examples. The results show that without temperature errors, the defect is accurately identified based on the phase and surface temperature. The random temperature error decreases the accuracy of the identification result based on the phase and surface temperature. The uniform temperature error decreases the accuracy of the identification result based on the surface temperature. However, the uniform temperature measurement error does not affect the identification result based on the phase.
- quantitative identification /
- comparative study /
- infrared detection /
- conjugate gradient

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图 1 二维热传导模型

Figure 1. The model of two-dimensional heat conduction

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图 2 基于相位和表面温度的CGM反演识别算法流程图

Figure 2. The flow chart of CGM inversion algorithm based on phase and surface temperature

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表 1 材料的物性参数

Table 1. Thermal properties of materials

Material	Specific heat capacity / (W/(m·℃))	Conductivity /(J/(kg·℃)	Density / (kg/m³)
Test-piece	117	963	2680
Air	0.0257	1005	1.2

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表 2 不同初始假设下的识别结果

Table 2. Results in different initial guesses

Initial guess/mm		Identification result/mm	E_r/(%)
15, 35, 5, 10	Phase detect	35.000, 25.000 10.000, 5.000	$2.1 \times {10^{ - 7}}$, $2.2 \times {10^{ - 8}}$ $3.3 \times {10^{ - 7}}$, $6.6 \times {10^{ - 7}}$
15, 35, 5, 10	Temperature detect	35.000, 25.000 10.000, 5.000	$3.4 \times {10^{ - 8}}$, $6.3 \times {10^{ - 9}}$ $1.2 \times {10^{ - 7}}$, $2.8 \times {10^{ - 8}}$
25, 15, 8, 8	Phase detect	35.000, 25.000	$1.0 \times {10^{ - 7}}$, $5.4 \times {10^{ - 8}}$
	Phase detect	10.000, 5.000	$3.6 \times {10^{ - 7}}$, $ 4.2 \times {10^{ - 8}} $
	Temperature detect	35.000, 25.000	$7.6 \times {10^{ - 9}}$, $8.0 \times {10^{ - 8}}$
	Temperature detect	10.000, 5.000	$6.8 \times {10^{ - 8}}$, $2.6 \times {10^{ - 8}}$
45, 15, 15, 01	Phase detect	35.000, 25.000	$9.7 \times {10^{ - 8}}$, $2.0 \times {10^{ - 7}}$
	Phase detect	10.000, 5.000	$1.9 \times {10^{ - 7}}$, $4.6 \times {10^{ - 7}}$
	Temperature detect	35.000, 25.000	$2.4 \times {10^{ - 7}}$, $2.1 \times {10^{ - 7}}$
	Temperature detect	10.000, 5.000	$2.0 \times {10^{ - 6}}$, $2.8 \times {10^{ - 7}}$

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表 3 不同随机误差下的识别结果

Table 3. Results in distinct random temperature errors

σ/℃		Identification result/mm	E_r/(%)
0	Phase detect	35.000, 25.000 10.000, 5.000	$2.1 \times {10^{ - 7}}$, $2.2 \times {10^{ - 8}}$ $3.3 \times {10^{ - 7}}$, $6.6 \times {10^{ - 7}}$
0	Temperature detect	35.000, 25.000 10.000, 5.000	$3.4 \times {10^{ - 8}}$, $6.3 \times {10^{ - 9}}$ $1.2 \times {10^{ - 7}}$, $2.8 \times {10^{ - 8}}$
0.2	Phase detect	34.970, 25.035	0.09, 0.14
	Phase detect	10.016, 4.996	0.16, 0.08
	Temperature detect	34.886, 25.017	0.33, 0.07
	Temperature detect	9.923, 5.008	0.77, 0.16
1	Phase detect	35.159, 24.912	0.45, 0.35
	Phase detect	10.117, 4.960	1.17, 0.80
	Temperature detect	34.653, 24.635	0.99, 1.46
	Temperature detect	9.764, 5.065	2.36, 1.30

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表 4 不同均匀误差下的识别结果

Table 4. Results in distinct uniform temperature errors

e/℃		Identification result/mm	E_r/(%)
0	Phase detect	35.000, 25.000 10.000, 5.000	$2.1 \times {10^{ - 7}}$, $2.2 \times {10^{ - 8}}$ $3.3 \times {10^{ - 7}}$, $6.6 \times {10^{ - 7}}$
0	Temperature detect	35.000, 25.000 10.000, 5.000	$3.4 \times {10^{ - 8}}$, $6.3 \times {10^{ - 9}}$ $1.2 \times {10^{ - 7}}$, $2.8 \times {10^{ - 8}}$
0.2	Phase detect	35.000, 25.000 10.000, 5.000	$2.1 \times {10^{ - 7}}$, $2.2 \times {10^{ - 8}}$ $3.3 \times {10^{ - 7}}$, $6.6 \times {10^{ - 7}}$
	Temperature detect	34.780, 24.829	0.63, 0.68
	Temperature detect	9.157, 5.028	8.43, 0.56
1	Phase detect	35.000, 25.000 10.000, 5.000	$2.1 \times {10^{ - 7}}$, $2.2 \times {10^{ - 8}}$ $3.3 \times {10^{ - 7}}$, $6.6 \times {10^{ - 7}}$
	Temperature detect	34.507, 24.124	1.41, 3.50
	Temperature detect	1.013, 5.077	89.87, 1.54

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