Underground Pipeline Surveillance with an Algorithm Based on Statistical Time-Frequency Acoustic Features

作     者：Tianlei Wang Jiuwen Cao Ru Xu Jianzhong Wang Tianlei Wang;Jiuwen Cao;Ru Xu;Jianzhong Wang

作者机构：the Artificial Intelligence Instituteand also with the Key Lab for IoT and Information Fusion Technology of ZhejiangHangzhou Dianzi UniversityHangzhou 310018China Zhejiang Sanhua Automotive Components Co.Ltd.Hangzhou 310008China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2022年第27卷第2期

页      面：358-371页

核心收录：

学科分类：0810[工学-信息与通信工程] 083305[工学-城乡生态环境与基础设施规划] 08[工学] 080401[工学-精密仪器及机械] 081403[工学-市政工程] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 081002[工学-信号与信息处理] 0814[工学-土木工程] 0833[工学-城乡规划学] 

基　　金：supported by the National Natural Science Foundation of China(Nos.U1909209 and 61503104) 

主　　题：underground pipeline surveillance time-frequency feature excavation device recognition Extreme Learning Machine(ELM) 

摘      要：Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid ***,designing a round-the-clock intelligent surveillance system has become crucial and *** this study,we develop an acoustic signal-based excavation device recognition system for underground pipeline *** front-end hardware system is equipped with an acoustic sensor array,an Analog-to-Digital Converter(ADC)module(ADS1274),and an industrial processor Advanced RISC Machine(ARM)cortex-A8 for signal collection and algorithm ***,a novel Statistical Time-Frequency acoustic Feature(STFF)is proposed,and a fast Extreme Learning Machine(ELM)is adopted as the *** on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum *** addition,the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.