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Tehnički vjesnik, Vol.24 No.6 Prosinac 2017.

Prethodno priopćenje
https://doi.org/10.17559/TV-20170523211205

A recognition algorithm to detect pipe weld defects

Wei Cui ; Northeast Petroleum University, Daqing high-tech development zone university street No. 99, Daqing City, Heilongjiang Province, China
Ke Wang ; Northeast Petroleum University, Daqing high-tech development zone university street No. 99, Daqing City, Heilongjiang Province, China
Qiang Zhang ; Northeast Petroleum University, Daqing high-tech development zone university street No. 99, Daqing City, Heilongjiang Province, China
Peng Zhang ; Petrochina Daqing Petrochemical Company, Mobile Equipment Department, Longfeng district, Daqing City, Heilongjiang Province, China

Puni tekst: engleski, pdf (1 MB) str. 1969-1975 preuzimanja: 0* citiraj
APA
Cui, W., Wang, K., Zhang, Q., Zhang, P. (2017). A recognition algorithm to detect pipe weld defects. Tehnički vjesnik, 24(6). doi:10.17559/TV-20170523211205
Puni tekst: hrvatski, pdf (1 MB) str. 1969-1975 preuzimanja: 0* citiraj
APA
Cui, W., Wang, K., Zhang, Q., Zhang, P. (2017). Algoritam raspoznavanja za otkrivanje grešaka u zavarima cijevi. Tehnički vjesnik, 24(6). doi:10.17559/TV-20170523211205

Sažetak
Taking magnetic flux leakage (MFL) imaging of pipe weld defects as the research object, a weld defect image recognition algorithm based on grey-gradient co-occurrence matrix (GGCM) and cluster analysis and mathematical morphology is proposed. Recognition of different types of welding defects was achieved. Firstly, a continuous non-contact scanning MFL system for the pipe weld was used to collect the three-dimensional MFL. Secondly, the three-dimensional MFL signal was converted to a two-dimensional greyscale image. Then the MFL image characteristics of the two-dimensional grayscale image were extracted using GGCM. Based on extracted image features, the characteristic quantity was analysed by using k-means clustering and then through the combination of histogram equalization, Otsu’s method of binaryzation, morphologically removing small objects, edge detection, and then structuring a morphologically optimized edge extraction method for edge detection on the grayscale. Through combination of several methods, a new algorithm to improve the detection effect was structured. The results indicated that this algorithm is adaptable and practical. This algorithm solved difficulties associated with the MFL method being used in the weld testing to realize the recognition of pipe weld defects and break through the applicable limitations of traditional signal processing technology.

Ključne riječi
defects; magnetic flux leakage imaging; pipe weld; recognition algorithm

Hrčak ID: 190197

URI
http://hrcak.srce.hr/190197

[hrvatski]

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