Automatic welding detection by an intelligent tool pipe inspection

11th International Conference on Damage Assessment of Structures

物理学;材料科学

Arizmendi, C.J.^1 ; Garcia, W.L.^2,3 ; Quintero, M.A.^3

Mechatronic Department, Universidad Autonoma de Bucaramanga, Avenue 42 No 48-11, Bucaramanga, Colombia^1

System Engineering Department, Universidad Autonoma de Bucaramanga, Avenue 42 No 48-11, Bucaramanga, Colombia^2

Corrosion Research Institute, Km 2 via Refugio, Piedecuesta, Colombia^3

关键词: Classification algorithm;    In-line inspection tools;    In-line inspections;    Noise reduction technique;    Oil-and-Gas pipelines;    Pipe welds;    Pre-processing algorithms;    Smart Pig;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/628/1/012082/pdf
DOI  :  10.1088/1742-6596/628/1/012082

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called "smart pig" in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.