A multi-time scale vibration surveillance system for third-party threats on urban pipeline

Third-party interference caused by construction activities have seriously jeopardized the security of underground pipelines. Following the process of "signal collection—feature extraction and selection—multi-time scale identifying— combining results by voting", this paper proposes a multi-time scale surveillance system for interference prevention of thirdparty threats on the nearby pipeline by using ground vibration monitors. The system focuses on the two major urban construction activities induced by excavator breaking hammers and road cutters, and presents excellent performance under the noise of traffic and pedestrian. Three features including the short-time zero-crossing rate, subset differential parameter and the Mel frequency cepstrum coefficients are selected by the analysis of the maximal information coefficient and feature importance for identifying the patterns of different third-party activities. The crucial part of the surveillance system consists of the two random forest-based classifiers trained by 0.5 s samples and 8 s samples respectively, and the alarm depends on the voting of the two classifiers, which brings the perspectives on different time scales for decision making. In the test, 96.14% of the threat vibration signals can be detected, while only 0.45% of the environmental noise signals cause false alarms.