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
– 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
– 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
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
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
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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