Quantitative Identification and Comparative Study of Defects Based on Phase and Surface Temperatures
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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。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.
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Key words:
- quantitative identification /
- comparative study /
- infrared detection /
- conjugate gradient
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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 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 Er/(%) 15, 35, 5, 10 Phase detect 35.000, 25.000
10.000, 5.000$2.1 \times {10^{ - 7}}$, $2.2 \times {10^{ - 8}}$
$3.3 \times {10^{ - 7}}$, $6.6 \times {10^{ - 7}}$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 $1.0 \times {10^{ - 7}}$, $5.4 \times {10^{ - 8}}$ 10.000, 5.000 $3.6 \times {10^{ - 7}}$, $ 4.2 \times {10^{ - 8}} $ Temperature detect 35.000, 25.000 $7.6 \times {10^{ - 9}}$, $8.0 \times {10^{ - 8}}$ 10.000, 5.000 $6.8 \times {10^{ - 8}}$, $2.6 \times {10^{ - 8}}$ 45, 15, 15, 01 Phase detect 35.000, 25.000 $9.7 \times {10^{ - 8}}$, $2.0 \times {10^{ - 7}}$ 10.000, 5.000 $1.9 \times {10^{ - 7}}$, $4.6 \times {10^{ - 7}}$ Temperature detect 35.000, 25.000 $2.4 \times {10^{ - 7}}$, $2.1 \times {10^{ - 7}}$ 10.000, 5.000 $2.0 \times {10^{ - 6}}$, $2.8 \times {10^{ - 7}}$ 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 Er/(%) 0 Phase detect 35.000, 25.000
10.000, 5.000$2.1 \times {10^{ - 7}}$, $2.2 \times {10^{ - 8}}$
$3.3 \times {10^{ - 7}}$, $6.6 \times {10^{ - 7}}$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 10.016, 4.996 0.16, 0.08 Temperature detect 34.886, 25.017 0.33, 0.07 9.923, 5.008 0.77, 0.16 1 Phase detect 35.159, 24.912 0.45, 0.35 10.117, 4.960 1.17, 0.80 Temperature detect 34.653, 24.635 0.99, 1.46 9.764, 5.065 2.36, 1.30 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 Er/(%) 0 Phase detect 35.000, 25.000
10.000, 5.000$2.1 \times {10^{ - 7}}$, $2.2 \times {10^{ - 8}}$
$3.3 \times {10^{ - 7}}$, $6.6 \times {10^{ - 7}}$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 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 1.013, 5.077 89.87, 1.54 -
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