The Enhancement of Leak Detection Performance for Water Pipelines through the Renovation of Training Data

Leakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent leak detection method based on a model using statistical parameters extracted from acoustic emission (AE) signals. Since leak signals depend on many operation conditions, the training data in real-life situations usually has a small size. To solve the problem of a small sample size, a data improving method based on enhancing the generalization ability of the data was proposed. To evaluate the effectiveness of the proposed method, this study used the datasets obtained from two artificial leak cases which were generated by pinholes with diameters of 0.3 mm and 0.2 mm. Experimental results show that the employment of the additional data improving block in the leak detection scheme enhances the quality of leak detection in both terms of accuracy and stability.

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

[2]  S. Moore.  A review of noise and vibration in fluid-filled pipe systems , 2016 .

[3]  T S ChanFelix,et al.  Leak detection of pipeline , 2012 .

[4]  Doreen Eichel,et al.  Learning And Soft Computing Support Vector Machines Neural Networks And Fuzzy Logic Models , 2016 .

[5]  Stephen G. MacDonell,et al.  Data quality in empirical software engineering: a targeted review , 2013, EASE '13.

[6]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[7]  T. Wren,et al.  Cross-correlation as a method for comparing dynamic electromyography signals during gait. , 2006, Journal of biomechanics.

[8]  Han Meng,et al.  Parameter selection in SVM with RBF kernel function , 2012, World Automation Congress 2012.

[9]  Hao Tian,et al.  A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox , 2011, Expert Syst. Appl..

[10]  Jian Li,et al.  A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines , 2016, Sensors.

[11]  J. Rafiee,et al.  A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system , 2009, Expert Syst. Appl..

[12]  Jian Li,et al.  Natural-gas pipeline leak location using variational mode decomposition analysis and cross-time–frequency spectrum , 2018, Measurement.

[13]  Jie Li,et al.  Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine , 2019, Measurement.

[14]  Maria Isabel Ribeiro,et al.  Gaussian Probability Density Functions: Properties and Error Characterization , 2004 .

[15]  Kilian Wasmer,et al.  Automatic detection of scuffing using acoustic emission , 2016 .

[16]  Manoj Kumar Tiwari,et al.  Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM , 2012, Expert Syst. Appl..

[17]  X. H. Wang,et al.  Wavelet packet analysis for the propagation of acoustic emission signals across turbine runners , 2009 .

[18]  Gérard-André Capolino,et al.  Advanced Diagnosis of Electrical Faults in Wound-Rotor Induction Machines , 2013, IEEE Transactions on Industrial Electronics.

[19]  Walid G. Morsi,et al.  A new reactive, distortion and non-active power measurement method for nonstationary waveforms using wavelet packet transform , 2009 .

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

[21]  M. Troncossi,et al.  Vibroacoustic Measurements for Detecting Water Leaks in Buried Small-Diameter Plastic Pipes , 2017 .

[22]  Yu Zhang,et al.  Leak detection monitoring system of long distance oil pipeline based on dynamic pressure transmitter , 2014 .

[23]  Shantanu Datta,et al.  A review on different pipeline fault detection methods , 2016 .

[24]  Jong-Myon Kim,et al.  Pipeline Fault Diagnosis Using Wavelet Entropy and Ensemble Deep Neural Technique , 2018, ICISP.

[25]  Martin J. Shepperd,et al.  Data quality: cinderella at the software metrics ball? , 2011, WETSoM '11.

[26]  William R. Nichols,et al.  Initial evaluation of data quality in a TSP software engineering project data repository , 2014, ICSSP 2014.

[27]  Jong-Myon Kim,et al.  Discriminative feature analysis based on the crossing level for leakage classification in water pipelines. , 2019, The Journal of the Acoustical Society of America.

[28]  Bijaya K. Panigrahi,et al.  Vibration Analysis Based Interturn Fault Diagnosis in Induction Machines , 2014, IEEE Transactions on Industrial Informatics.

[29]  Benjamin Naumann,et al.  Learning And Soft Computing Support Vector Machines Neural Networks And Fuzzy Logic Models , 2016 .

[30]  Bruno Brunone,et al.  Pipe system diagnosis and leak detection by unsteady-state tests. 1. Harmonic analysis , 2003 .

[31]  Jong-Myon Kim,et al.  Leakage Classification Based on Improved Kullback-Leibler Separation in Water Pipelines , 2019, FDSE.

[32]  Kok Kiong Tan,et al.  Fault Diagnosis and Fault-Tolerant Control in Linear Drives Using the Kalman Filter , 2012, IEEE Transactions on Industrial Electronics.

[33]  Deborah J. Brewer.  Theme Introduction -- Leak Detection: A Money-Saving Expense (PDF) , 1979 .

[34]  Cheng Siong Chin,et al.  Review of Current Technologies and Proposed Intelligent Methodologies for Water Distributed Network Leakage Detection , 2018, IEEE Access.

[35]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[36]  Amiya R Mohanty,et al.  Monitoring gear vibrations through motor current signature analysis and wavelet transform , 2006 .

[37]  Mehrdad Sharif Bakhtiar,et al.  LEAK DETECTION IN WATER-FILLED PLASTIC PIPES THROUGH THE APPLICATION OF TUNED WAVELET TRANSFORMS TO ACOUSTIC EMISSION SIGNALS , 2010 .

[38]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[39]  Wieslaw J. Staszewski,et al.  Comparative study of instantaneous frequency based methods for leak detection in pipeline networks , 2012 .

[40]  D. Bianchi,et al.  Wavelet packet transform for detection of single events in acoustic emission signals , 2015 .