Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning
Abstract
:1. Introduction
2. Related Work
2.1. Existing Nondestructive Testing Devices for Risers
2.2. Research on Intelligent Identification of Pipeline Defects
3. Materials and Methods
3.1. Development and Application of Magnetic Memory-Based Riser Inspection Robots
3.1.1. Design Requirements
3.1.2. Design Proposal
3.1.3. Industrial Application
3.1.4. Data Visualization Analysis
3.2. Intelligent Classification of Defect Images Based on SK-ConvNeXt-KAN
3.2.1. ConvNeXt Network
- Macro design
- Micro design
3.2.2. Selective Kernel Attention
- Split
- Fuse
- Select
3.2.3. Kolmogorov–Arnold Network
3.2.4. SK-ConvNeXt-KAN Network
4. Experiment Results and Discussion
4.1. Experimental Environment
4.2. Dataset and Evaluation Metrics
4.3. Comparative Experiment
4.4. Ablation Study
- SK-ConvNeXt
- ConvNeXt-KAN
- SK-ConvNeXt-KAN
4.5. Generalization Ability Test
4.5.1. Fivefold Cross-Validation
4.5.2. Cross-Dataset Validation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Institution | Detector | Method | Type | Features |
---|---|---|---|---|
Dutch scholar | RTD | Pulsed Eddy Current | External inspection | Water depth of 150 m |
Brazil | SIRIS | Visual, Ultrasonic, Radiographic | External inspection | Water depth of 250 m |
TechnipFMC | IRIS | Electromagnetic, Ultrasonic, and X-ray Inspection | External inspection | Production platforms |
Weatherford | ORIS | Ultrasonic Testing | Internal inspection | In situ inspection |
UPC | ACFM | Alternating Current Field Measurement | External inspection | In situ inspection |
CUP | MMM | Metal Magnetic Memory | Internal inspection | Offline inspection |
Output Size | ResNet-50 | ConvNeXt-T | |
---|---|---|---|
stem | 56 × 56 | 7 × 7, 64, stride 2 3 × 3 max pool, stride 2 | 4 × 4, 96, stride 4 |
Stage1 | 28 × 28 | ||
Stage2 | 14 × 14 | ||
Stage3 | 56 × 56 | ||
Stage4 | 7 × 7 | ||
FLOPS |
Experimental | Configuration | Training Options | Value |
---|---|---|---|
Deep Learning Frameworks | PyTorch 1.10.0 | Optimizer | AdamW |
Programming Language | Python 3.8 | Loss Function | Cross-Entropy |
CPU | Intel® Core (TM) I5-13500H | Epoch | 100 |
GPU | NVIDIA GeForce RTX 3060 (6 GB) | Batch size | 16 |
RAM | 32 GB | Initial Learning Rate | 0.0005 |
No. | Label | Defect Types | Number | Proportion |
---|---|---|---|---|
1 | Pit | Pitting | 270 | 24.3% |
2 | Gls | Groove Loss | 148 | 13.3% |
3 | Lgw | Longitudinal Wear | 252 | 22.7% |
4 | Ire | Irregular Flaw | 141 | 12.7% |
5 | Gwd | Girth Weld | 299 | 26.9% |
Total | 1110 | 100% |
Model | Training Accuracy (%) | Validation Accuracy (%) |
---|---|---|
GoogLeNet | 97.6 | 83.4 |
ResNet-50 | 94.6 | 90.7 |
ViT | 90.4 | 90.4 |
Swin-T | 84.1 | 92.3 |
ConvNeXt | 91.2 | 91.8 |
SK-ConvNeXt-KAN | 94.5 | 95.4 |
Model | Accuracy (%) | Precision (%) | Recall (%) | F1 Score |
---|---|---|---|---|
Baseline | 91.8 | 88.43 | 88.55 | 0.884 |
SK-ConvNeXt | 93.6 | 90.86 | 90.57 | 0.907 |
ConvNeXt-KAN | 92.7 | 90.03 | 89.13 | 0.895 |
SK-ConvNeXt-KAN | 95.4 | 91.68 | 91.88 | 0.917 |
Experiment | Accuracy (%) | Precision (%) | Recall (%) | F1 Score |
---|---|---|---|---|
Fold-1 | 94.55 | 91.55 | 91.53 | 0.915 |
Fold-2 | 95.45 | 93.66 | 92.50 | 0.930 |
Fold-3 | 95.00 | 92.70 | 92.54 | 0.926 |
Fold-4 | 94.09 | 91.01 | 91.13 | 0.910 |
Fold-5 | 95.91 | 93.93 | 92.61 | 0.932 |
Average | 95.00 | 92.57 | 92.06 | 0.923 |
No. | Label | Defect Types | Train_Num | Validate_Num |
---|---|---|---|---|
1 | In | Inclusions | 240 | 60 |
2 | Pa | Patches | 240 | 60 |
3 | Cr | Cracks | 240 | 60 |
4 | PS | Pitted Surfaces | 240 | 60 |
5 | RS | Rolled-in Scale | 240 | 60 |
6 | Sc | Scratches | 240 | 60 |
Total | 1800 |
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Liu, X.; Fan, J. Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning. Sustainability 2024, 16, 7389. https://doi.org/10.3390/su16177389
Liu X, Fan J. Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning. Sustainability. 2024; 16(17):7389. https://doi.org/10.3390/su16177389
Chicago/Turabian StyleLiu, Xiangyuan, and Jianchun Fan. 2024. "Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning" Sustainability 16, no. 17: 7389. https://doi.org/10.3390/su16177389