【摘要】 对管道第三方活动进行振动监测和预警可以显著提高管道安全性。遵循“数据采集-样本分割-特征提取-识别模型训练-识别策略”框架，建立了基于随机森林算法的埋地管道光纤周界振动监测系统。通过长度为5.35 km的相位敏感光时域反射计（phase-sensitive optical time-domain reflectometer，简称φ-OTDR）光纤传感系统，采集了鹤嘴锄、铲子、锤子和电锤4种典型周界入侵活动的振动信号和85 h时长的环境振动信号。依据信号对比分析结果，选择合理的样本分割尺度和特征提取方法，并训练随机森林识别模型。提出了时空矩阵识别策略用于识别模型的结果修正，减少了99.59%的系统误报。在测试中，光纤周界振动监测系统的识别率为94.87%，误报率仅为0.013 9%，这说明该系统能够抵抗城市中常见的环境振动干扰。更多还原

【Abstract】 The detection of third-party activities near pipes based on vibration signals can effectively enhance pipeline safety. In the framework of“data collection-sample segmentation-feature extraction-recognition model training-recognition strategy”,a random forest pipeline perimeter monitoring system by vibration optical fiber is proposed. The vibration signals of pickaxe,spade,hammer and electric hammer are collected by 5.35 km φ-OTDR optical fiber,and the 85 h environmental signals are also collected for comparison. The methods of sample segmentation and feature extraction are established to train a random forest model. A strategy of space-time matrix is proposed for correcting the model result,which will reduce the false alarm by 99.59%. In the test,the recognition rate of the perimeter monitoring system is 94.87%,while the false alarm rate is only 0.013 9%,which indicates that the system performs well under common urban environmental vibrations.更多还原