Improving the Ability of a Laser Ultrasonic Wave-Based Detection of Damage on the Curved Surface of a Pipe Using a Deep Learning Technique
Abstract
:1. Introduction
2. Ultrasonic Wave Generation Mechanism Using Pulsed Laser
2.1. Ultrasonic Wave Mode Generation Theory
2.2. Ultrasonic Wave Propagation Imaging System Configuration
2.3. Ultrasonic Wave Propagation Imaging Algorithm
3. Deep Learning-CNN
3.1. CNN
3.2. Object Detection
3.3. EfficientDet
4. UWPI-System-Based Pipe Damage Detection Experiment and CNN Learning
4.1. Detecting External Damage to Pipe Bends Using UWPI System
4.2. CNN Learning Using Damage Data
4.2.1. Transfer Learning
4.2.2. Train Dataset
4.2.3. Training Dataset
4.2.4. UWPI Data Deep Learning Result
5. Conclusions
- Through additional experiments and research, we intend to secure UWPI data according to the damage size using laser scanning techniques for the components (curved part, curved pipe part, bolted joint part, welding, etc.) of pipes.
- This study confirmed the possibility of detecting damage to pipes based on laser scanning through the transfer learning technique, and based on this, we intend to propose a better detection technique using new algorithms and large amounts of data.
- To acquire ultrasonic signals in the laser scanning system, this study used the AE sensor installed directly on the pipe. Therefore, we intend to develop a noncontact nondestructive system for efficient pipe damage detection by using laser diameter vibration (LDV) instead of an AE sensor.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Laser Head: Brilliant Ultra GRM100 | Galvanometer: Scancube 10 |
---|---|
Wavelength: 1064 nm | Wavelength: 1064 nm |
Energy per pulse: 100 mJ | Tracking error: 0.16 ms |
Pulse repetition rate: 20 Hz | Positioning speed: 10 m/s |
Pulse duration: 6.5 ns | Max. angular velocity: 100 rad/s |
Beam diameter: 3 mm | (within 0.35 rad) |
Batch Size | Steps | Epochs | No. of Samples |
---|---|---|---|
8 | 10,000 | 80 | 1000 |
8 | 30,000 | 240 | 1000 |
8 | 50,000 | 400 | 1000 |
Test Image | Step 10,000 | Step 30,000 | Step 50,000 |
---|---|---|---|
1 | 79% | 89% | 97% |
2 | 79% | 88% | 96% |
3 | 79% | 90% | 97% |
4 | 78% | 91% | 96% |
5 | 77% | 91% | 98% |
6 | 72% | 90% | 98% |
7 | 73% | 90% | 94% |
8 | 77% | 89% | 94% |
9 | 77% | 89% | 94% |
10 | 77% | 90% | 92% |
11 | 78% | 87% | 84% |
12 | 77% | 87% | 92% |
13 | 82% | 83% | 86% |
14 | 85% | 88% | 90% |
15 | 86% | 85% | 89% |
16 | 85% | 81% | 89% |
17 | 85% | 80% | 91% |
18 | 82% | 75% | 88% |
19 | 76% | 76% | 90% |
20 | 66% | 76% | 94% |
21 | 67% | 81% | 91% |
22 | 66% | 83% | 90% |
23 | 62% | 78% | 95% |
24 | 59% | 78% | 93% |
25 | 58% | 73% | 89% |
26 | 67% | 77% | 88% |
27 | 68% | 72% | 85% |
28 | 68% | 70% | 81% |
29 | 58% | 72% | 87% |
30 | 67% | 74% | 83% |
31 | 71% | 80% | 75% |
32 | 73% | 83% | 68% |
33 | 77% | 90% | 85% |
34 | 80% | 92% | 94% |
35 | 82% | 92% | 94% |
36 | 82% | 92% | 94% |
37 | 83% | 91% | 94% |
38 | 84% | 93% | 94% |
39 | 86% | 93% | 96% |
40 | 87% | 94% | 99% |
41 | 87% | 94% | 98% |
42 | 87% | 92% | 98% |
43 | 88% | 93% | 97% |
44 | 88% | 95% | 98% |
45 | 88% | 94% | 98% |
46 | 88% | 94% | 97% |
47 | 88% | 95% | 98% |
48 | 89% | 96% | 98% |
49 | 86% | 95% | 95% |
50 | 85% | 95% | 96% |
51 | 82% | 93% | 96% |
52 | 85% | 93% | 96% |
53 | 87% | 93% | 96% |
54 | 88% | 93% | 98% |
55 | 89% | 93% | 98% |
56 | 88% | 94% | 97% |
57 | 85% | 92% | 96% |
58 | 82% | 93% | 94% |
59 | 83% | 93% | 94% |
60 | 84% | 90% | 92% |
61 | 84% | 92% | 96% |
62 | 83% | 91% | 96% |
63 | 83% | 94% | 93% |
64 | 83% | 92% | 92% |
65 | 80% | 92% | 94% |
66 | 82% | 92% | 94% |
67 | 82% | 89% | 96% |
68 | 81% | 87% | 94% |
69 | 77% | 89% | 97% |
70 | 77% | 90% | 97% |
71 | 77% | 87% | 92% |
72 | 63% | 68% | 64% |
73 | 51% | 57% | 0% |
74 | 0% | 66% | 52% |
75 | 58% | 69% | 0% |
76 | 50% | 70% | 53% |
77 | 0% | 53% | 0% |
78 | 0% | 56% | 58% |
79 | 67% | 76% | 50% |
80 | 87% | 93% | 98% |
Average detection rate | 75% | 86% | 88% |
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Yu, B.; Tola, K.D.; Lee, C.; Park, S. Improving the Ability of a Laser Ultrasonic Wave-Based Detection of Damage on the Curved Surface of a Pipe Using a Deep Learning Technique. Sensors 2021, 21, 7105. https://doi.org/10.3390/s21217105
Yu B, Tola KD, Lee C, Park S. Improving the Ability of a Laser Ultrasonic Wave-Based Detection of Damage on the Curved Surface of a Pipe Using a Deep Learning Technique. Sensors. 2021; 21(21):7105. https://doi.org/10.3390/s21217105
Chicago/Turabian StyleYu, Byoungjoon, Kassahun Demissie Tola, Changgil Lee, and Seunghee Park. 2021. "Improving the Ability of a Laser Ultrasonic Wave-Based Detection of Damage on the Curved Surface of a Pipe Using a Deep Learning Technique" Sensors 21, no. 21: 7105. https://doi.org/10.3390/s21217105
APA StyleYu, B., Tola, K. D., Lee, C., & Park, S. (2021). Improving the Ability of a Laser Ultrasonic Wave-Based Detection of Damage on the Curved Surface of a Pipe Using a Deep Learning Technique. Sensors, 21(21), 7105. https://doi.org/10.3390/s21217105