Technical Code Analysis of Geomagnetic Flaw Detection of Suppression Rigging Defect Signal Based on Convolutional Neural Network
Source abstract
Abstract
In this paper, technical code analysis and recognition of the defect signal of the suppression rigging based on a convolutional neural network are carried out given the difficulty and low recognition rate of the defect detection and recognition of the suppression rigging. Firstly, the magnetic induction signal of the suppression rigging defects is collected using CM-801 (Anshan, China), Kalman filtering is used to screen and pre-process the collected data, and the noise reduction data are presented in the form of a cloud image. The pressed rigging defect data set is constructed, and the region of broken wire defect and stress in the image is calibrated. The single-stage object detection algorithm YOLOv5 (You Only Look Once) based on convolutional neural network model calculation is used, the scale detection layer and positioning loss function of the YOLOv5 algorithm are improved and optimized, and the improved YOLOv5 algorithm is used for experiments. The experimental results show that the detection accuracy of the convolution neural network model can reach 97.1%, which can effectively identify the defect signal of the suppressed rigging.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Non-Destructive Testing Techniques · Machine Fault Diagnosis Techniques · Fatigue and fracture mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Metal Material for Marine Equipment and Application, Anshan 114051, China
School of Materials and Metallurgy, University of Science and Technology Liaoning, Anshan 114051, China
School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Funding
National Natural Science Foundation of China · State Key Laboratory of Metal Material for Marine Equipment and Applications
References (41)
Show source-provided references
1. Development status and Suggestions of China’s offshore engineering Equipment Manufacturing industry
2. Weischedel, H.R., and Chaplin, C.R. (1992, January 12–14). The Inspection of Offshore Wire Ropes: The State-of-the-Art. Proceedings of the Offshore Technology Conference, Shanghai, China.10.4043/6969-MS ↗
3. Quantitative method for detecting internal and surface defects in wire rope10.1016/j.ndteint.2021.102405 ↗
4. Mazurek, P. (2023). A comprehensive review of steel wire rope degradation mechanisms and recent damage detection methods. Sustainability, 15.10.3390/su15065441 ↗
5. Study of steel wire ropes defects10.4028/www.scientific.net/AMM.683.55 ↗
6. Discrimination method of wire rope fault signal based on Holzer sensor for multi array weak magnetic detection10.1007/s10586-018-2440-4 ↗
7. Wire Rope Damage Detection Based on Magnetic Leakage and Visible Light10.1007/s11668-023-01682-z ↗
8. A review of wire rope detection methods, sensors and signal processing techniques10.1007/s10921-020-00732-y ↗
9. The state-of-art of quantitative nondestructive testing of wire ropes
10. Residual magnetic field for identification of damage in steel wire rope
11. Detection method of broken magnetic leakage Hall Effect based on Structural Characteristics of wire rope
12. A transducer made up of fluxgate sensors for testing wire rope defects10.1109/19.989914 ↗
13. Kim, J.-W., and Park, S. (2018). Magnetic flux leakage sensing and artificial neural network pattern recognition-based automated damage detection and quantification for wire rope non-destructive evaluation. Sensors, 18.10.3390/s18010109 ↗
14. Theory and numerical analysis of extreme learning machine and its application for different degrees of defect recognition of hoisting wire rope10.1155/2018/4168209 ↗
15. Defect Detection of Steel Wire Rope in Coal Mine Based on Improved YOLOv5 Deep Learning
16. Visual sensing inspection for the surface damage of steel wire ropes with object detection method10.1109/JSEN.2022.3214109 ↗
17. Wang, W., Chen, J., Han, G., Shi, X., and Qian, G. (2024). Application of Object Detection Algorithms in Non-Destructive Testing of Pressure Equipment: A Review. Sensors, 24.10.3390/s24185944 ↗
18. Automatic detection of industrial wire rope surface damage using deep learning-based visual perception technology
19. Comparison and analysis of multiple signal processing methods in steel wire rope defect detection by hall sensor10.1016/j.measurement.2020.108768 ↗
20. Zhou, P., Zhou, G., Zhu, Z., He, Z., Ding, X., and Tang, C. (2019). A review of non-destructive damage detection methods for steel wire ropes. Appl. Sci., 9.10.3390/app9132771 ↗
21. Zhang, G., Tang, Z., Fan, Y., Liu, J., Jahanshahi, H., and Aly, A.A. (2021). Steel wire rope surface defect detection based on segmentation template and spatiotemporal gray sample set. Sensors, 21.10.3390/s21165401 ↗
22. Analysis of Steel wire rope fracture accident and Preventive Measures of Port Crane
23. Tension monitoring and defect detection by magnetostrictive longitudinal guided wave for fine wire rope
24. Fault and error tolerance in neural networks: A review10.1109/ACCESS.2017.2742698 ↗
25. Liu, S., and Deng, W. (2015, January 3–6). Very deep convolutional neural network based image classification using small training sample size. Proceedings of the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia.10.1109/ACPR.2015.7486599 ↗
26. Telçeken, M., Akgun, D., and Kacar, S. (2024). An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images. Appl. Sci., 14.10.3390/app142311293 ↗
27. Xu, W., Fang, H., Yu, S., Yang, S., Yang, H., Xie, Y., and Dai, Y. (2024). RSNC-YOLO: A Deep-Learning-Based Method for Automatic Fine-Grained Tuna Recognition in Complex Environments. Appl. Sci., 14.10.3390/app142210732 ↗
28. Hu, H., Chen, M., Huang, L., and Guo, C. (2024). BHI-YOLO: A Lightweight Instance Segmentation Model for Strawberry Diseases. Appl. Sci., 14.10.3390/app14219819 ↗
29. Kalman filtering. Embedded systems programming
30. An optimization approach to adaptive Kalman filtering10.1016/j.automatica.2011.04.004 ↗
31. Visual multi-target tracking based on adaptive Kalman filter
32. A comparative study and review of different Kalman filters by applying an enhanced validation method10.1016/j.est.2016.10.004 ↗
33. Guo, L., Kang, Z., Ding, S., Yuan, X., Yang, H., Zhang, M., and Wang, S. (2024). A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity. Appl. Sci., 14.10.3390/app14219913 ↗
34. Patrick, U., Rao, S.K., Jagan, B.O.L., Rai, H.M., Agarwal, S., and Pak, W. (2024). Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter. Appl. Sci., 14.10.3390/app14188332 ↗
35. Zhang, Y., Guo, Z., Wu, J., and Tian, Y. (2022). Real-time vehicle detection based on improved yolo v5. Sustainability, 14.10.3390/su141912274 ↗
36. Yan, B., Fan, P., Lei, X., Liu, Z., and Yang, F. (2021). A real-time apple targets detection method for picking robot based on improved YOLOv5. Remote Sens., 13.10.3390/rs13091619 ↗
37. Ju, M., Luo, H., Wang, Z., Hui, B., and Chang, Z. (2019). The application of improved YOLO V3 in multi-scale target detection. Appl. Sci., 9.10.3390/app9183775 ↗
38. Cheng, B., Girshick, R., Dollár, P., Berg, A.C., and Kirillov, A. (2021, January 19–25). Boundary IoU: Improving object-centric image segmentation evaluation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.01508 ↗
39. Kim, H.-J., Eesaar, H., and Chong, K.T. (2024). Transformer-Enhanced Retinal Vessel Segmentation for Diabetic Retinopathy Detection Using Attention Mechanisms and Multi-Scale Fusion. Appl. Sci., 14.10.3390/app142210658 ↗
40. Ding, S., Jing, W., Chen, H., and Chen, C. (2024). Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss. Appl. Sci., 14.10.3390/app14177493 ↗
41. Wang, J., Xu, C., Yang, W., and Yu, L. (arXiv, 2021). A normalized Gaussian Wasserstein distance for tiny object detection, arXiv.