Hybrid method for enhancing acoustic leak detection in water distribution systems: Integration of handcrafted features and deep learning approaches

[1]  B. Cai,et al.  Fault Diagnosis Methodology of Redundant Closed-Loop Feedback Control Systems: Subsea Blowout Preventer System as a Case Study , 2023, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[2]  Isack Thomas Nicholaus,et al.  One-Class Convolutional Neural Networks for Water-Level Anomaly Detection , 2022, Sensors.

[3]  Srinivas Koppu,et al.  Smart Water Resource Management Using Artificial Intelligence—A Review , 2022, Sustainability.

[4]  D. Butler,et al.  The role of deep learning in urban water management: A critical review. , 2022, Water research.

[5]  B. Cai,et al.  Optimal sensor placement methodology of hydraulic control system for fault diagnosis , 2022, Mechanical Systems and Signal Processing.

[6]  Jong-Myon Kim,et al.  A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning , 2022, Sensors.

[7]  Zhoumo Zeng,et al.  Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector , 2021 .

[8]  Xue Wu,et al.  Leakage Detection in Water Distribution Systems Based on Time–Frequency Convolutional Neural Network , 2021, Journal of Water Resources Planning and Management.

[9]  Kumaraswamy Ponnambalam,et al.  Ensemble-based machine learning approach for improved leak detection in water mains , 2021, Journal of Hydroinformatics.

[10]  Di Meng,et al.  Enhanced spectrum convolutional neural architecture: An intelligent leak detection method for gas pipeline , 2020 .

[11]  Alireza Alghassi,et al.  Deep Learning Model for Industrial Leakage Detection Using Acoustic Emission Signal , 2020, Informatics.

[12]  Kalyan R. Piratla,et al.  Leakage detection in water pipelines using supervised classification of acceleration signals , 2020 .

[13]  Shuming Liu,et al.  Novel Leakage Detection and Localization Method Based on Line Spectrum Pair and Cubic Interpolation Search , 2020, Water Resources Management.

[14]  Tao Tao,et al.  Disturbance Extraction for Burst Detection in Water Distribution Networks Using Pressure Measurements , 2020, Water Resources Research.

[15]  Bingpeng Zhou,et al.  Machine-Learning-Based Leakage-Event Identification for Smart Water Supply Systems , 2020, IEEE Internet of Things Journal.

[16]  Roya A. Cody,et al.  Linear Prediction for Leak Detection in Water Distribution Networks , 2020 .

[17]  Viviana Meruane,et al.  Experimental investigation into techniques to predict leak shapes in water distribution systems using vibration measurements , 2018 .

[18]  Jiheon Kang,et al.  Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems , 2018, IEEE Transactions on Industrial Electronics.

[19]  Suzhen Li,et al.  Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition , 2018 .

[20]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[21]  Shuming Liu,et al.  A review of data-driven approaches for burst detection in water distribution systems , 2017 .

[22]  Xue Wu,et al.  Burst detection in district metering areas using a data driven clustering algorithm. , 2016, Water research.

[23]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[24]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[25]  Zoran Kapelan,et al.  A review of methods for leakage management in pipe networks , 2010 .

[26]  Bryan W. Karney,et al.  A selective literature review of transient-based leak detection methods , 2009 .

[27]  F. Schlindwein,et al.  A study on the optimum order of autoregressive models for heart rate variability. , 2002, Physiological measurement.

[28]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[29]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[30]  Osama Hunaidi,et al.  Acoustical characteristics of leak signals in plastic water distribution pipes , 1999 .

[31]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[32]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.