Systems for pipeline leakage detection

Four university teams ­ University of Bradford, University of East Anglia, Brunel University and Imperial College ­ have formed a research consortium examining important inter-related aspects of monitoring, modelling and leakage management in the water distribution network. Professor I Torsun from the University of Bradford¹s Department of Computing details the research project.

Leak detection carried out by water companies is focused on analysis of night flow measurements in district metering areas. The measurements are almost always manual and use a number of methods including leak correlation, step testing and the use of electronic listening sticks. These methods, apart from noise correlation, are time consuming, labour intensive and unreliable.

- to investigate the causes of failure of buried infrastructure through the collection and analysis of historical data;

- to develop a neural network and statistical computer system which will continuously, automatically and centrally monitor a pipeline distribution network;

- to refine the failure detection system to include a component for the prediction of pipeline bursts and water quality and computing consequences of failure and cost benefit analysis of pipe replacement; and

- to disseminate and exploit the research output effectively.

The project brings together mathematical and theoretical foundation, advanced computational and statistical models, engineering, management and economic expertise within the consortium.

Signal capture and low-cost data acquisition present major challenges, being required for all the signals collected. The monitoring systems envisaged need data from a number of sensors distributed over the network to provide snapshots of its state. A proper snapshot requires data from each sensor to be time stamped so that the network can be viewed as a whole at giveninstants. High resolution time-stamping of data is therefore a high priority. An initial study of this problem indicates that use of the Rugby radio time signal may provide a solution.

As well as collecting data, the planned active network sounding methods introduce an acoustic signal into the pipe that can be detected and acquired at other locations within the locale. The low level acoustic signal generated will be carefully crafted, based on pseudo-random binary sequences and is designed to enable the acoustic propagation characteristics of the pipe network to be determined. The hardware needed to generate and inject these signals will be integrated with the data acquisition systems. Data obtained using the acquisition systems will be stored in a raw binary form so that future processing schemes can be applied to the original data if necessary. A second part of the data collection activity will be the design and development of a temporal-spatial database to store pipeline signatures and associated feature sets.

Historical data (hydraulic and water quality) obtained from Yorkshire Water, Anglian Water and Thames Water, as well as on-line dynamic data generated from Yorkshire Water will provide sufficient volume of data to characterise the overall behaviour of a pipeline distribution network (pipeline signature). In addition, correlation-based leak detection using acoustic methods will be used. Examples include the use of knowledge about the acoustic transmission properties of the pipe to compensate for frequency dispersion effects in the evaluation of cross-correlation functions. It is known for studies at the University of East Anglia that uncompensated dispersion can severely impair the detection of leaks by this method. Another example is the use of adaptive filtering methods coupled with neural net pattern processing to remove extraneous interfering noise from the acoustic signals used during cross correlation which is already known to be a problem in plastic pipes. Both methods will be developed during the project.

The signature observed on a network will be used as an indicator of normal, fault-free or abnormal operation. Discrimination between normal and abnormal signatures will be analysed using neural nets and statistical methods. In the case of neural nets signature analysis, unsupervised self-organising an supervised neural networks will be cascaded to learn and extract the salient features of the data in normal and abnormal conditions. In the case of statistical signature analysis, the probability distribution of observed signatures is estimated and used to assess the likelihood of a particular observation.

Techniques to allow the neural network to Œlearn¹ by being exposed to acceptable and unacceptable patterns of signals will be researched and optimised. Included in this part of the research will be the effect of natural variations (eg diural) and long-term pipeline degradation effects. The features used in the signature will be actual values of normalised pressure and flow coupled with a compact feature of the acoustic signal such as a correlation function. However, these features must be fused together in a principled manner such that discriminatory features are not overpowered by less discriminatory or noisy features. Simple approaches (eg variance normalisation, principal component analysis) are strengthened by the use of class discriminatory transformations of the feature using both analytic and neural net methods. In both neural net and statistical approaches to signature analysis, rigorous methods or statistical cross validation will be used to ensure that results are meaningful.

Interpretation of pipeline signature will be carried out using neural networks and statistical computing models. Neural networks architecture will be determined from the nature of the problem to be solved which includes monitoring requirements (water leaks, pipe deformation, joint failure, cracks, obstructions etc) and pipeline signature. Initially research will focus on a temporal adaptive neural network. This type of neural network is able to learn without being given correct answers for the input pattern. It is effective with problems whole algorithms are too complex to define, and is able to compensate for accuracy and noise in sensor readings. It offers a method for dealing with unexpected and changing situations and allows for incremental learning. These properties match favourable with the irregular, noisy and unexpected pattern of sensor signals we envisage obtaining from the pipeline. The neural network will be trained with Œseen' data to ensure its stability and convergence. Random faults will be generated in the pipeline, and then the network will be tested with unseen data to determine the distributive effect of one or more Œfaults' propagating through the pipeline. Statistical methods will be used to interpret and validate acoustic signal and the neural network.

While a neural network is a powerful recognition and learning technique, it can neither generate actions from the knowledge it has learned nor can it interpret the knowledge. An effective method to solve this problem is to generate rules from knowledge learned by the neural network and then use these rules to construct a knowledge-based expert system that can interpret data, generate actions and give explanations. The University of Bradford team is developing a system which automatically generates a rule-set from a neural network. It uses an optimisation algorithm based on cluster analysis to eliminate redundant rules. It is proposed to further develop the system to generate a rule-set from the neural network, which can be used to provide recommendations, actions and explanations of pipeline behaviour. The approach of network signature analysis is complementary to the methods proposed by the consortium teams at Brunel University and Imperial College. These are essentially based on Œstrong' analytic models of the network, whereas those of University of East Anglia and University of Bradford are based on Œreal¹ data models that are based on large amounts of training data taken from the network.

The efficient management of leakage in water distribution networks is high on the agenda of the UK water industry. This research proposal has been met with enthusiastic response from the collaborating water companies because it addresses problems which are currently either impossible or very difficult to solve successfully.

The programme of research is designed to generate a series of incremental deliverables that will allow the industry and consumers to benefit both in the short and long term. For example, a short-term deliverable will be an enhanced acoustic correlation system working well on plastic pipes, other materials and trunk mains. The more ambitious concepts of local network signature analysis is a medium-term goal that may allow leaks to be detected remotely over a wide area and has the potential to save water and reduce operating costs. The concept of a highly instrumented pipe installation using low cost sensors and efficient data capture is a long-term goal that would enable failures to be predicted before they occur. This would be of great benefit in saving water and reducing costs to consumers and companies alike.

In addition to these tangible goals, the research is likely to lead to the development of a family of signal processing and signature analysis techniques that may find application in domains other than the water industry, such as the petrochemical, gas and power distribution networks.


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