基于支持向量机的燃气管道泄漏识别方法研究

Research on Gas Pipeline Leakage Identification Method Based on Support Vector Machine

智能检测方法应用到管道泄漏检测的效果缺少相应的验证,为此,开展了支持向量机的泄漏检测机器人的开发和研究。研究的检测机器人搭载声学传感器,采集并识别燃气管道内的声学信号,通过支持向量机算法实现对于燃气管道泄漏的判断。研究结果表明:当燃气管道发生泄漏时,检测机器人所采集的声信号在时域上幅值会有明显增加,且距离泄漏点越近其幅值越大,在频域上幅值位于1800 Hz和3000 Hz附近时明显增大;支持向量机算法可以用来判断燃气管道是否发生了泄漏,检测精度能够达到98%;基于支持向量机(SVM)机器学习算法,对燃气管道泄漏的声信号进行训练和测试,能够精确地对燃气管道泄漏进行识别,弥补了基于时域和频域判断泄漏的不足,提高了管道泄漏检测器的检测精度和自主识别分析能力。研究结论可以为燃气管道的维护检测提供技术参考。

The effect of application of intelligent detection method to pipeline leakage detection lacks corresponding verification.Therefore,the support vector machine(SVM)based leakage detection robot was developed.The developed detection robot carried acoustic sensors,collected and identified the acoustic signals in the gas pipeline,and used the support vector machine algorithm to realize the judgment of gas pipeline leakage.The research results show that when the gas pipeline leaks,the amplitude of acoustic signals collected by the detection robot increases significantly in time domain,the closer to the leakage point,the greater the amplitude is,and the amplitude increases significantly in frequency domain at 1,800 and 3,000 Hz;the support vector machine algorithm can be used to judge whether the gas pipeline leaks,and the detection accuracy can reach 98%;and the support vector machine based machine learning algorithm improves the detection accuracy and autonomous identification and analysis ability of pipeline leakage detector.The research conclusions provide technical references for the maintenance and detection of gas pipelines.