基于支持向量机的矿浆管道堵塞信号识别方法

Support Vector Machine Based Recognition Method for Pipeline Blockage

针对传统统计学理论的局限性,提出一种基于支持向量机的矿浆管道堵塞信号识别方法。该方法可以有效地识别矿浆管道中压力信号、流量信号的异常,通过分析压力信号、流量信号的异常从而准确识别堵塞信号。对于矿浆管道堵塞发生的早期发现起到一个很好的预警效果。研究结果表明,该方法分类效果好,泛化能力强,在识别正确率上优于径向基核函数人工神经网络方法,为矿浆管道安全生产监测提供了可靠的理论支持。

An SVM-based recognition method is discussed for the blockage of slurry pipelines.The method can effectively identify slurry pipeline pressure signal abnormalities,by analyzing the pressure signals to accurately identify the blockage signals,and can give an early warning for slurry pipeline clogging.The research results show that the proposed method has better classification performance and generalization ability,provide support for the slurry pipeline safety production monitoring.