用智能管道检测仪信号自动识别焊缝，准确率90%至98%
Automatic welding detection by an intelligent tool pipe inspection
- 通过预处理降噪与特征选择提升信号质量
- 采用神经网络与支持向量机模型，识别准确率达90%-98%
- 适用于油气管道智能巡检，适合工业安全场景
本文基于机器学习技术，利用油气管道内检测工具（智能猪）获取的信号实现焊缝识别。模型首先通过预处理算法和特征选择进行信号降噪，相关降噪方法经文献调研与实测数据验证筛选。随后使用人工神经网络与支持向量机等分类算法训练模型，并通过交叉验证与ROC分析评估性能。实验结果表明，该模型可实现焊缝的自动识别，准确率在90%至98%之间。
原文摘要 · Abstract (English)
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 preprocessing algorithms and attributeselection 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
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