Leak Detection In Water Pipelines Research Articles

Thermal response to background leakages around an actively heated fiber optic sensor for leak detection in water distribution mains: modeling the effect of heating power and time

Fiber optic distributed temperature sensing (DTS) has been recently proposed as a promising technology to detect leaks in water infrastructure. However, it requires a suitable temperature difference between leaked water and the subsurface. Coupling active heating with fiber optic DTS could overcome this limitation, but very limited information is available on its application to leak detection in water pipelines. In this regard, this study addresses for the first time the use of active distributed temperature sensing (ADTS) to detect background leakages, ubiquitous and persistent leaks responsible for large losses and practically undetectable with conventional technologies. The 3D transient analysis of the thermal response to background leakages under different heating powers and times shows that there is potential to detect and locate incredibly small leaks (∼ L/d) with a detection threshold of 3 °C when actively heating a fiber optic sensor placed at distance from the pipe, in a location suitable for new and existing pipelines as well as for passive sensors. A potential to quantify background leakages also exists, since nonlinear relationships linking temperature alterations to the leak rate were predicted. The analysis further shows that it is crucial to determine for how long the detection threshold is overcome, and that increasing the heating power is not necessarily the answer. Finally, forced convection is the main heat transfer mechanism around the active sensor once reached by leaked water, and it appears that the chance of altering temperature and quality of water flowing within the nearby pipe and surrounding soil/groundwater would be very limited with ADTS.

Development and experimental testing of an innovative nondestructive thermal sensor utilizing the thermal interrogation method for detecting leakage in water plastic pipes

Leak detection in water pipelines is still an important issue in real-world industrial systems. Thus, a new nondestructive thermal sensor design based on the thermal interrogation method has been developed to detect and estimate the real-time leaks in water plastic pipes without environmental interference. The proposed thermal interrogation method utilizes a combination of heat flux and temperature measurements on the upstream and downstream outside plastic pipe surfaces. These measurements are obtained by using heat flux sensors and thermocouples that are covered by thin-film polyimide electric heaters. The interrogation method approach delivers that at constant input heat flux, the difference in sensor temperature values is dependent on the leakage volume flow rates. The sensor was tested over a range of water leakage volume flow rate from 0.1 m3/h to 1.3 m3/h and a range of water temperature from 21 °C to 25 °C. The water leakage volume flow rates were correlated to the difference in temperature values between the upstream and downstream sensors. The measurement results reveal that this sensor can be used to detect and estimate the real-time water leakage volume flow rate with high accuracy and repeatability for plastic pipes.

Comparative Analysis of Machine Learning and Deep Learning Based Water Pipeline Leak Detection Using EDFL Sensor

Comparative Analysis of Machine Learning and Deep Learning Based Water Pipeline Leak Detection Using EDFL Sensor

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

AbstractEffective water pipeline leak detection and localization can help water practitioners save water losses and reduce pipe rehabilitation budgets. Different sensing technologies can provide essential data aiding model development for predicting future leaks in the water distribution networks. Solutions for applying different sensing technologies to real water distribution networks are being developed for real‐time data collection, precise leak localization, reducing false alarms, and continuous pipeline monitoring. Experiments of different scales and specifications have been conducted to test the application and effectiveness of sensing technologies for leak diagnosis in water pipelines. Quality data collection is especially crucial for the successful implementation of these experiments. However, practitioners need help designing effective leak detection and localization experiments, wasting time, and resources. There needs to be a greater understanding of the design parameters for more accurate testing, such as sensor selection, sensor placement, ambient noise sources, and system operations. The current study, therefore, delineates design parameters for design leak diagnosis experiments for lab, testbed, and real networks. For application in real water networks, expert opinion is used to help identify the benefits and limitations and the implementation issues of available sensing technologies. An example experiment design using multi‐sensing technologies for smart leak diagnostics in Hong Kong is also presented. The study can guide water practitioners and researchers to design a multi‐sensing leak diagnostic experiments enabling a smart and sustainable utility management.This article is categorized under: Engineering Water > Methods Engineering Water > Sustainable Engineering of Water Science of Water > Water Extremes

Water Pipeline Leak Detection Based on a Pseudo-Siamese Convolutional Neural Network: Integrating Handcrafted Features and Deep Representations

The detection of leaks in water distribution systems (WDS) has always been a major concern for urban water supply companies. However, the performance of traditional leak detection classifiers highly depends on the effectiveness of handcrafted features. An alternative method is to use a convolutional neural network (CNN) to process raw signals directly to obtain deep representations that may ignore prior information about the leakage. The study proposes a novel approach to leak detection in WDS using ground acoustic signals, and demonstrates the effectiveness of combining handcrafted features and deep representations using a pseudo-siamese convolutional neural network (PCNN) model. Mel frequency cepstral coefficient (MFCCs) are selected as additional handcrafted features to traditional time- and frequency-domain (TFD) features. Based on the results of the model performance evaluation, the optimized PCNN model performs better than other methods, with an accuracy of 99.70%. A quantitative analysis of the PCNN demonstrates the effectiveness of handcrafted features and deep representations. Model visualization and interpretation analysis show that feature fusion occurs in the feedforward of the PCNN, hence improving the model’s performance. The present work can effectively support the development of novel intelligent leak detection equipment for WDS.

Hydroinformatics based technique for leak identification purpose – An experimental analysis

Analysis of single point pressure transient signal has the potential of providing information on the pipeline system. The technique uses the frequency response and experiments are satisfactory for low-frequency bandwidth. The concept behind this approach is that reflections are detected by leak-induced disturbances. Such reflections are often very difficult to identify, particularly because of excessive noise. The common approach to boost the analysis is to use a data denoising approach. The wavelet transforms for leak detection in water pipelines are proposed in this article. The wavelet filter is optimally calibrated using maximal kurtosis values and is chosen for leakage detection. The suggested approach is tested using high-noise laboratory test signals. The findings show that the method is superior to current signal processing methods for the conditions used. The system permits not only the detection of leakage but also the location and evaluation of its magnitude with errors of less than 7%.

Low-Cost, Tiny-Sized MEMS Hydrophone Sensor for Water Pipeline Leak Detection

In this paper, we present an experimental investigation of a water pipeline leak detection system based on a low-cost, tiny-sized hydrophone sensor fabricated using the microelectromechanical system (MEMS) technologies. A 10 × 10 element arrayed MEMS hydrophone device with chip size of 3.5 × 3.5 mm $^2$ was used in the experiment. The hydrophone device is packaged with a customized on-board preamplification circuit using an acoustic transparent material. The overall package size of the MEMS hydrophone is $\Phi$ 1.2 × 2.5 cm. The packaged MEMS hydrophone achieves an acoustic sensitivity of −180 dB (re: 1 V/ $\mu$ Pa), a bandwidth from 10 Hz to 8 kHz, and a noise resolution of around 60 dB (re: 1 $\mu \text{Pa/}\sqrt{\text{Hz}}$ ) at 1 kHz. A section of ductile iron water pipeline with an internal diameter of 10 cm, wall thickness of 0.73 cm, and length of 30 m is constructed as the test bed for the water leak detection. Two different leak sizes with leak flow rates of about 30 and 180 L/min are designed along the pipe, which is pressurized at 3.2 bar. Analysis of the transient signals and spectrograms shows that the MEMS hydrophone can capture the key acoustic information of the water leak, i.e., identifying the leak and locating the leak position. The measurement results demonstrate the feasibility to construct an affordable, highly efficient, real-time, and permanent in-pipe pipeline health monitoring network based on the MEMS hydrophones due to their high performance, low cost, and tiny size.

Experimental evaluation of a vibration-based leak detection technique for water pipelines

Conventional water pipeline leak detection surveys employ labour-intensive acoustic techniques, which are usually expensive and not amenable for continuous monitoring of distribution systems. Many previous studies attempted to address these limitations by proposing and evaluating a myriad of continuous, long-term monitoring techniques. However, these techniques have difficulty to identify leaks in the presence of pipeline system complexities (e.g. T-joints), offered limited compatibility with popular pipe materials (e.g. PVC), and were in some cases intrusive in nature. Recently, a non-intrusive pipeline surface vibration-based leak detection technique has been proposed to address some of the limitations of the previous studies. This new technique involves continuous monitoring of the change in the cross-spectral density of surface vibration measured at discrete locations along the pipeline. Previously, the capabilities of this technique have been demonstrated through an experimental campaign carried out on a simple pipeline set-up. This paper presents a follow-up evaluation of the new technique in a real-size experimental looped pipeline system located in a laboratory with complexities, such as junctions, bends and varying pipeline sizes. The results revealed the potential feasibility of the proposed technique to detect and assess the onset of single or multiple leaks in a complex system.

Research on Leak Detection of Water Pipeline Base on PSO-SVM

Leakage of water supply pipeline will cause waste of water resources and economic losses, the traditional support vector machine (SVM) had two free parameters (include kernel parameter g and penalty factor C from the radial basis function kernel) which could lead the classifier in an instability situation. In this paper, a method was presented to establish a SVM model which used the particle swarm optimization algorithm (PSO) to determine the two unknown parameters. Particle swarm optimization is an intelligent computational swarm optimization method, which optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. The method not only has strong global search capability, but also easily to implement. In order to improve the accuracy of the SVM model, the PSO algorithm is used to optimize the parameters of SVM. A prediction model of water pipeline leak detection based on PSO-SVM is established after the sample set is been collected, trained, tested and classified. The experimental results showed that with the strong global search capability of PSO, the free parameters of SVM was been set effectively and accuracy of the classifier had been promoted.

SmartPipes: Smart Wireless Sensor Networks for Leak Detection in Water Pipelines

Asset monitoring, specifically infrastructure monitoring such as water distribution pipelines, is becoming increasingly critical for utility owners who face new challenges due to an aging network. In the UK alone, during the period of 2009–2010, approximately 3281 mega litres (106) of water were wasted due to failure or leaks in water pipelines. Various techniques can be used for the monitoring of water distribution networks. This paper presents the design, development and testing of a smart wireless sensor network for leak detection in water pipelines, based on the measurement of relative indirect pressure changes in plastic pipes. Power consumption of the sensor nodes is minimised to 2.2 mW based on one measurement every 6 h in order to prolong the lifetime of the network and increase the sensor nodes’ compatibility with current levels of power available by energy harvesting methods and long life batteries. A novel pressure sensing method is investigated for its performance and capabilities by both laboratory and field trials. The sensors were capable of measuring pressure changes due to leaks. These pressure profiles can also be used to locate the leaks.