Excavation equipments classification based on short-time frame acoustic energy ratio

Underground pipeline network suffered severe breakages caused by external road excavation equipments in the mainland of China in nowadays. The frequent underground pipeline network breakages caused serious economic loss to urban living and industry manufacturers. Therefore, designing a surveillance system which can automatically detect potential damages caused by excavation equipments is essential and urgent for underground pipeline network system protection. With this objective, we investigate two frequently used excavation equipments, namely, electric hammer and cutting machine, based on acoustic signal processing in this paper. The acoustic waves generated by these two devices are analyzed and a novel statistical feature named short-time frame acoustic energy ratio (SFER) is developed for signal characterization. The approximated distributions of the SFER for both electric hammer and cutting machine have been estimated with real recorded data. A likelihood ratio test based on Neyman-Pearson (NP) approach is then designed to perform signal classification. For performance verifications, real recorded acoustic signals for electric hammer and cutting machine on a metro construction site are conducted in experiments with our proposed signal classification approach. Experimental resutls on various set-ups have been provided to show the effectiveness of our proposed method.