Plug Valve Surface Defects Identification through Nondestructive Testing and Fuzzy Deep-Learning Algorithm for Metal Porosity and Surface Evaluation
This paper addresses the detection and identification of flaws in Plug valves. The Plug valve thermal image is acquired using a Fluke thermal camera [TiS20]. Thermal images of the Plug valve are used for the identification of flaws such as Crack, Porosity, Corrosion, and Internal defects. The therma...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/2420903 |
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| Summary: | This paper addresses the detection and identification of flaws in Plug valves. The Plug valve thermal image is acquired using a Fluke thermal camera [TiS20]. Thermal images of the Plug valve are used for the identification of flaws such as Crack, Porosity, Corrosion, and Internal defects. The thermal images detect the surface flaws and never subsurface flaws in Plug valves. The subsurface flaws detection is a challenging problem in valve inspection. In this paper, the thermal images obtained after the dye penetrates the surface valve detect the surface flaws more efficiently after applying the Fuzzy Deep Learning Algorithms. DyePenetrating Test (DPT) combined with Infrared Thermography is proposed to identify heat flux changes and flaws in the faulty metal surface of Plug valves. In DPT, thinned paint is employed on the metal surface that displays metal porosity and even fine cracks. After DPT, thermal images of the Plug valve are processed through the Fuzzy Deep Learning Algorithm to evaluate flaws. The Fuzzy Algorithm is utilized prior to Deep Learning to simplify and speed up the classification task. The flaws are identified using Slicing operation and the following parametric quantities such as Accuracy, Mathew’s Correlation Coefficient (MCC), Local Self-Similarity Descriptor (LSS), Precision/Recall, F-measure, and Jaccard Index. The parametric quantities depict corresponding variations with regard to surface coarseness and metal flaws. The DPT and Fuzzy Deep Learning Algorithm identify metal defects with 80.67% accuracy. |
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| ISSN: | 2314-4912 |