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首页> 外文会议>International Conference on Electrical, Control and Automation Engineering >Leakage Detection in Pipelines Using Decision Tree and Multi-Support Vector Machine
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Leakage Detection in Pipelines Using Decision Tree and Multi-Support Vector Machine

机译：使用决策树和多支撑向量机管道泄漏检测

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摘要

In order to solve the problem of leakage detection in the case of complex conditions and limited training samples, a multivariate classification recognition model was built by using Decision Tree and Support Vector Machine, which has advantages of rapid speed and high efficiency in classification and outstanding characteristics in small samples binary classification. The model was trained with a fault feature vector which is a dimensionless value extracted from the pipeline pressure signal characteristic parameters, and then using the model to test the samples. The results show that this method not only can complete the model learning training in the case of small samples, but also has been greatly improved over the neural network method in terms of the recognition performance, and can be effectively applied to leakage detection in pipelines.
机译：为了解决复杂条件和有限的训练样本的情况下解决泄漏检测问题，采用决策树和支持向量机建造了多变量分类识别模型，其具有快速速度和高效率的分类和出色特性的优点 在小样本二进制分类中。 该模型用故障特征向量训练，这是从流水线压力信号特性参数提取的无量纲值，然后使用模型来测试样品。 结果表明，这种方法不仅可以在小样本的情况下完成模型学习培训，而且在识别性能方面也在神经网络方法上大大提高，并且可以有效地应用于管道中的泄漏检测。

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