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Tsinghua Science and Technology 2022, 27(2): 358-371 https://doi.org/10.26599/TST.2020.9010055
Open Access | Issue | Published: 29 September 2021

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

Show Author's Information Tianlei Wang,  Jiuwen Cao( ),  Ru Xu,  Jianzhong Wang
Artificial Intelligence Institute
Zhejiang Sanhua Automotive Components Co. Ltd., Hangzhou 310008, China
Key Lab for IoT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou 310018, China
Keywords:
Extreme Learning Machine (ELM), underground pipeline surveillance, time-frequency feature, excavation device recognition
Cite this article:
Wang T, Cao J, Xu R, et al. Underground Pipeline Surveillance with an Algorithm Based on Statistical Time-Frequency Acoustic Features. Tsinghua Science and Technology, 2022, 27(2): 358-371. https://doi.org/10.26599/TST.2020.9010055
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Abstract Full text About this article

Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization. Thus, designing a round-the-clock intelligent surveillance system has become crucial and urgent. In this study, we develop an acoustic signal-based excavation device recognition system for underground pipeline protection. The 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 implementation. Then, a novel Statistical Time-Frequency acoustic Feature (STFF) is proposed, and a fast Extreme Learning Machine (ELM) is adopted as the classifier. Experiments on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum features. In addition, the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.


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About this article

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

Show Author's information Tianlei Wang, Jiuwen Cao( ), Ru Xu, Jianzhong Wang
Artificial Intelligence Institute
Zhejiang Sanhua Automotive Components Co. Ltd., Hangzhou 310008, China
Key Lab for IoT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou 310018, China

Abstract

Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization. Thus, designing a round-the-clock intelligent surveillance system has become crucial and urgent. In this study, we develop an acoustic signal-based excavation device recognition system for underground pipeline protection. The 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 implementation. Then, a novel Statistical Time-Frequency acoustic Feature (STFF) is proposed, and a fast Extreme Learning Machine (ELM) is adopted as the classifier. Experiments on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum features. In addition, the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.

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

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Publication history

Received: 23 September 2020
Revised: 06 October 2020
Accepted: 22 October 2020
Published: 29 September 2021
Issue date: April 2022

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© The author(s) 2022

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. U1909209 and 61503104).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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