Leak detection in water distribution pipes using singular spectrum analysis

ABSTRACT

Small leaks in buried water distribution pipelines run continuously for long periods of time without being detected. They do not produce any appreciable flow or pressure changes at the monitored locations. The non-stationarity of the monitoring data, background noise, and the uncertainties in interpreting sensory information adds complexity to detecting leaks. This paper explores the application of singular spectrum analysis (SSA) in extracting leak components from noisy measurements. SSA is a non-parametric and adaptive method, able to decompose a signal into interpretable components without making linearity or stationarity assumptions. When applied to noisy hydro-acoustic signals, it is shown that the leak signatures are extracted efficiently. A semi-supervised approach for leak detection is presented, in which the SSA decomposition of leak-free historical data is combined with ensemble one-class support vector machine. The results demonstrate the effectiveness of SSA for leak detection in water distribution pipelines.     

Burst detection using hydraulic data from water distribution systems with artificial neural networks

This paper presents research into the application of artificial neural networks (ANNs) for analysis of data from sensors measuring hydraulic parameters (flow and pressure) of the water flow in treated water distribution systems. Two neural architectures (static and time delay) are applied for time series pattern classification from the perspective of detecting leakage. Results are presented using data from an experimental site in a distribution system of a UK water company in which bursts were simulated by hydrant flushing. Field trials have shown how ANNs can be used effectively for a leakage detection task. Both static and time delay ANNs learned patterns of leaks/bursts. The time delay neural network showed improved performance over the static network. It is concluded that the effectiveness of an ANN in discovering relationships within the data is dependent upon two key factors: availability of sufficient exemplars and data quality.     

Water pipeline failure detection using distributed relative pressure and temperature measurements and anomaly detection algorithms

This paper presents the validation of a novel leak detection method for water distribution pipelines, although it could be applied to any buried pressurized fluid flow pipe. The detection method is based on a relative pressure sensor attached non-invasively to the outside of the pipe combined with temperature difference measurements between the pipe wall and the soil. Moreover, this paper proposes an anomaly detection algorithm, originally developed for monitoring website traffic data, which differentiates a ‘leak’ event from ‘normal’ pressure change events. It is compared to two more commonly used methods based on a fixed threshold and a moving average. The validation of the new system in a field trial over a 6-month period showed that all the known leaks were identified with 98.45% accuracy, with the anomaly detection algorithm performing best, making this system a real contender for leak detection in pipes.     

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.     

Novel vibration-based technique for detecting water pipeline leakage

Exacerbating the imbalance between demand for freshwater and available water resources is the sub-optimal performance of water distribution systems, which are plagued with leaks that cause significant losses of treated freshwater. This paper presents an approach for leak detection that involves continuous monitoring of the changes in the correlation between surface acceleration measured at discrete locations along the pipeline length. A metric called leak detection index is formulated based on cross-spectral density of measured pipe surface accelerations for detecting the onset and assessing the severity of leaks. The proposed non-invasive approach requires minimal human intervention and works under normal operating conditions of the pipeline system without causing any operational disturbances. The approach is demonstrated on a 76 mm diameter polyvinyl chloride pipeline test system considering varying leak severities. The preliminary results presented in this paper seem promising and lead to several interesting questions that will require further research.     

Pipe network leak detection: comparison between statistical and machine learning techniques

ABSTRACT

This paper investigates an inverse analysis technique to find leaks in water networks and compares different solution strategies. Although a number of strategies have been proposed by different authors to identify leaks on a vast selection of pipe networks, limited research has been done to compare strategies and point out their weakness. Three strategies, a Bayesian probabilistic analysis, a support vector machine and, an artificial neural network were combined with the inverse analysis technique on different numerical and experimental networks to point out each strategy’s weakness. Two numerical networks are investigated and one experimental network. It is shown that the Bayesian probabilistic analysis struggles to find unique solutions when a few observations are available, while the support vector machine and the artificial neural network struggle when only flow measurements are available. Additionally it is shown that the artificial neural network struggles to estimate unique solutions for leak size and location.     

Perturbation mapping of water leak in buried water pipes via laboratory validation experiments with high-frequency ground penetrating radar (GPR)

This paper studies the perturbation patterns of GPR images as a tool for water leakage detection in buried water pipes with laboratory experiments. Different perturbations patterns on GPR signals due to a water leak of metallic and PVC pipes buried in a sand box, were mapped and studied with controlled water injection and leak volume, as well as a fixed leak position in the pipes. These perturbation patterns of signal strength include the tale-tell signs of a central leak point and propagation of the radial wetting front vortex centered around the leak point at different injection times. These patterns, compared to the no-leak dry condition, were interpreted with the conventional principles of dielectric contrast and reflection coefficients, and the associated reflection and absorption mechanisms. It is believed that this set of data will serve as an image matching fingerprint to identify and map water leaks in the field.     

Multi-tier method using infrared photography and GPR to detect and locate water leaks

This paper presents newly developed method for detecting and locating leaks in water distribution networks utilizing two detection techniques; ground penetrating radar (GPR) and infrared photography (IR). The experimental work and field investigation were carried out over 2 years in three locations in City of Doha, Qatar to capture 115 IR image frames and 23 GPR image frames. Firstly, GPR technology is utilized to accurately define location of buried pipes. After locating these pipes, IR images are collected for simulated and actual leaks. The developed algorithm segments each image into leakage and non-leakage areas and the centroid of each leakage is calculated using Green's theorem. Subsequently, GPR images are introduced as a second layer and overlaid with IR images to compare pipes location with leak location. The method was successfully applied to detect simulated and actual leaks in summer and winter seasons with small margin of error (2.9–5.6%) in estimating leakage areas. When examining the investigated four operating conditions, it was found that the developed method can predict leaks in a more reliable way if the camera height is 2 m and the speed is 1.65 m/s in both simulated and actual leaks. The newly developed method is robust and can aid operators and city engineers in detecting and locating water leaks with high accuracy.     

Automated leak localization performance without detailed demand distribution data

Automatic leak localization has been suggested to reduce the time and personnel efforts needed to localize (small) leaks. Yet, the available methods require a detailed demand distribution model for successful calibration and good leak localization performance. The main aim of this work was to analyze whether such a detailed demand distribution is needed. Two demand distributions were used: a factorized distribution that distributes the inflow demand proportionally across the consumption nodes according to individual billing data, and a uniform distribution that equally distributes demand across all consumption nodes. The performance of the automatic leak localization method, using both demand distribution models, was compared. A new measure for leak localization performance that is based on the percentage of false positive nodes is proposed. It was possible to localize the leaks with both demand distribution models, although performance varied depending on the timing and duration of the measurement.     

A numerical investigation into the effect of pressure on holes and cracks in water supply pipes

The effect of water pressure in a pipe on the rate of leakage from leak openings in the pipe is one of the main factors influencing leakage that is still not understood sufficiently. In this study, the behaviours of different types of leak openings (round holes and longitudinal and circumferential cracks) on pressurized pipes were investigated for different pipe materials (uPVC, steel, cast iron and asbestos cement) using finite element analysis. Linear elastic behaviour was assumed. The study found that (1) pipe stresses are significantly affected by a leak opening, and can easily exceed the material's yield strength in the vicinity of the opening; (2) round holes show the smallest expansion with pressure, followed by circumferential cracks and then longitudinal cracks; (3) the areas of all leak openings increase linearly with pressure; (4) longitudinal pipe stresses affect the behaviours of round holes and circumferential cracks, but not that of longitudinal cracks; and (5) the effect of pressure on a leak opening increases exponentially with increasing hole diameter or crack length. An equation is proposed for modelling the effect of pressure on individual leaks.     

基于RSSI 算法的城市燃气管网泄漏检测及定位研究

针对城市燃气管道泄漏问题,提出了一种基于RSSI算法的城市燃气管道泄漏检测与定位技术。该技术可以分析和计算管道泄漏时ZigBee 节点之间接收信号的强度,并最终获得泄漏的位置。首先利用泄漏目标点到各接受点之间的RSSI 信号强度值确立算法模型,其次通过模型得出泄漏点到各接收点之间的距离,最后通过最小二乘法获得泄漏点的近似坐标。仿真结果显示,提出的算法具有较高的定位精度和广泛的应用前景。     

Leakage detection and location in water distribution systems using a fuzzy-based methodology

Loss of water due to leakage is a common phenomenon observed practically in all water distribution systems (WDS). However, the leakage volume can be reduced significantly if the occurrence of leakage is detected within minimal time after its occurrence. This paper proposes a novel methodology to detect and diagnose leakage in WDS. In the proposed methodology, a fuzzy-based algorithm has been employed that incorporates various uncertainties into different WDS parameters such as roughness, nodal demands, and water reservoir levels. Monitored pressure in different nodes and flow in different pipes have been used to estimate the degree of membership of leakage and its severity in terms of index of leakage propensity (ILP). Based on the degrees of leakage memberships and the ILPs, the location of the nearest leaky node or leaky pipe has been identified. To demonstrate the effectiveness of the proposed methodology, a small distribution network was investigated which showed very encouraging results. The proposed methodology has a significant potential to help water utility managers to detect and locate leakage in WDS within a minimal time after its occurrence and can help to prioritise leakage management strategies.     

城市供水管网漏损探讨

就漏损控制这一项复杂的系统工程，详细地介绍了我国城市供水管网的漏损现状，分析了进行漏损控制产生的效益，造成漏损的主要原因及防治措施，提出漏损量的大小是衡量供水管网技术技术和运行的重要指标。     

城市供水管网漏损时间的预测模型研究

城市供水企业迫切需要加强给水管网的漏损管理，以减少漏损水量和提高经济效益。在对华北某市供水管网漏损数据进行统计和分析的基础上，按照管段实际发生漏损次数分两种情况建立了供水管网漏损时间的预测模型，对漏损次数≤4次的管段采用基于SAS系统的多元线性回归方法，对漏损次数〉4次的管段则采用灰色预测方法。经实例验证，多元线性回归方法预测的平均相对误差为21％，灰色预测方法预测的平均相对误差〈6％，整套模型的精度可满足城市供水管网漏损宏观管理的需要，能够提高管网漏损防治的效率。     

A new development in locating leaks in sanitary sewers

A new method called focused electrode leak location system (FELL-41) was developed in Europe for identifying potential leaks in sanitary sewers (mainline and service connections at the mainline). The chief advantages of the FELL-41 technology include: (1) identifying leaking joints on mainline during dry weather; (2) prioritising leak repairs by intensity of leaks; (3) determining leaks in service lateral mainline connections; (4) an alternative to air-pressure testing for acceptance of new and rehabilitated sanitary sewers. The FELL-41 method measures electrical current flow between a probe that travels in the pipe and a surface electrode. Pipe defects that allow liquids to flow into or out of the pipe cause a spike in the electrical signal, thereby locating the sources of infiltration or exfiltration. The intensity of the measured current can be correlated to the magnitude of the leaks. The purpose of this paper is to discuss this technology as well as its limitations. The results of the FELL-41 technology will be validated through several case studies.     

余姚市供水管网漏损形成的原因及对策研究

以余姚市供水管网漏损为例,通过对管网漏损形成原因的分析,从建立免费用水水量统计、加强管网管理、加强计量管理等方面,提出了相应的对策及措施,以确保供水管网漏损率大幅度降低。     

分区计量供水管网水力模型的流量分配

结合天津市供水管网实例,通过分析其计量水量的特点与水力模型的构建要求,按照水量数据来源分别侧重于小区表、户表和在线流量计,提供了分区计量供水管网水力模型的三个流量分配方案;从数据健全度、流量分配准确度、实施难度、流量分配校正依据、漏损考察功能、模型动态模拟、模型维护与应用难度和模型构建平台八个方面对三个流量分配方案进行了多角度评价,可为水力模型项目的实施提供参考。     

供水损失产生的机理与控制

介绍了供水损失产生的机理,并对其产生原因进行了分析,结合国内的一些具体经验,从流量监测、管网检漏、合理选用管材和管网改造技术等方面着手,提出了控制供水损失的措施,以提高供水企业的运行效益。     

塑料管道磨损性破坏事故的分析

PE、PVC管材因其优良特性而在流体输送系统中得到广泛应用，但其某些部位易发生破漏事故。介绍了PE输送矿砂系统与PVC输水系统短时间内发生多次漏损的案例，分析了磨损事故的原因，指出流体扰动对管壁的磨损是管道产生破漏的原因，其实质是管道设计的缺陷所致。     

寒区隧道新型维护型防排水系统研究

目前的寒区隧道防排水系统设计主要倾向于功能设计,而对于防排水的可维护性设计则很少。当隧道发生渗漏时,渗漏源头不易确定,处理起来难度大,费工费时。文章提出背贴充气式止水带和分贴充气式止水带,在渗漏发生时能准确判断渗漏部位,同时实现止水带与混凝土的密贴又能够防止渗水纵向流窜,并由此建立衬砌段可维护性防水的系统;提出中埋充气式止水带,解决普通止水带与混凝土粘结力差的问题,同时,该止水带在接缝发生位移错动后仍能通过再充气实现与混凝土的密贴,并由此建立接缝段可维护性防水系统;提出纵向排水井检查设计方法,解决纵向排水管因结冰或者泥砂淤积的影响,并由纵向排水井建立可维护性排水系统。