CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a National Stage of International patent application PCT/EP2016/065212, filed on Jun. 29, 2016, which claims priority to foreign European patent application No. EP 15306029.8, filed on Jun. 29, 2015, the disclosures of which are incorporated by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates to the detection of anomalies in a water distribution system. More specifically, it relates to the detection of anomalies with the combined use of statistics and a hydraulic model of the water distribution system.

BACKGROUND PRIOR ART

A system for distributing drinkable water is notably made of pipes between a water head and consumers, along with control devices such as valves and pumps. Such system can be subject to numerous anomalies. Anomalies belong to a plurality of types. Hydraulic anomalies comprise leaks, abnormal variation of pressure, fast drop of the water level of a reservoir, incoherent mass balance of storage. Operation anomalies define an element in the system which is in an incorrect state, for example a valve in a opening state different to that stored in the information system. These anomalies, especially leaks, can dramatically reduce the performance of the water distribution system. For example, leaks in the pipes are the cause of the loss of a significant part of the water between water head and consumers, and can make structural damages. The detection and correction of anomalies in a water distribution system is therefore a permanent concern of the operators of such systems in order to mitigate the economic cost of water loss and damages. Moreover, the detection of leaks in a water distribution system is a key objective for limiting the global water consumption and waste, which is of particular interest in regions subject to water stress, and in view of promoting sustainable development.

The detection of leaks in a water distribution system is historically performed through human inspection. Human inspection generally consists in sending human operators to inspect pipes of the system and identify leaks and other anomalies. This detection can for example be aided using audio sensors, which detect noise due to a leak. However, the typically large size of water distribution systems renders human detection of leaks and anomalies very difficult. For example, the water distribution system of a large city comprises thousands of km of pipes. It is therefore impossible to inspect all pipes frequently at a reasonable cost.

The use of sensors contributes to a solution to the issue of detecting anomalies in a water distribution system. Sensors can notably be used for automatically detecting abnormal changes in the behavior of the system, therefore human operators can be sent to those nodes/arcs of the system wherein abnormal behavior was detected. However, sensor-based detection methods of anomalies have disadvantages as well. They can generate a high number of false positive (alarms for events which are not actual anomalies), and lead to numerous useless and costly human interventions. They can also properly detect anomalies but with imprecise localization, due to the large scale of a typical water distribution system. Otherwise, deploy a high density of sensors in the system could help in locating anomalies more precisely, but it is too costly.

The U.S. Pat. No. 8,635,051 discloses a method for identifying leaks in a water distribution system. This method comprises using a model of the leaks, and iteratively applying this model in order to obtain predicted values of water consumption, comparing the output values to observations, updating the values in order to obtain a better model, until the difference between predictions and observations is below a threshold, or a limit of the number of iterations is reached. The use of an hydraulic model by this method permits to identify a leak in a node wherein no sensor is available. However, it lacks an historical comparison for comparing observations to historical data. Thus, the robustness of the prediction is lowered, since a threshold for detecting a leak or abnormal value needs to be defined a priori.

There is therefore a need for a method for improving the robustness of automatic detection and localization of anomalies in a water distribution system. The proposed method is based on the use of a hydraulic model, and a statistical processing of time series of measurements at selected nodes of the system.

SUMMARY OF THE INVENTION

To this effect, the invention discloses a method for detecting anomalies according to claim1, a system for detecting anomalies according to claim14and computer program product according to claim15. Dependent claims define preferred embodiments of the present invention.

The invention also discloses a method for detecting anomalies in a water distribution system composed of a network of nodes, said method comprising: A) parametrizing a hydraulic model of the water distribution system by initializing a set of values of control variables characterizing the network and its output at the nodes; B) using sensors on the network to acquire observations of a subset of the state variables, said observations having time references; C) changing the set of values of the control variables; D) using the hydraulic model to calculate predicted values of a set of state variables characterizing at least water velocity and pressure at the nodes at the time references; E) computing residue values of the set of state variables as a difference between predicted values and observations at the time references; F) if said differences satisfy a break criterion, going to step H); G) if not, changing the set of values of the control variables and going back to step D); H) if said differences satisfy a refinement criterion, going to step J); I) if not, selecting an subset of the network where to calculate predicted values, going back to step D); J) classifying an entity of the network in a state according to the set of control variables.

Advantageously, steps C) to J) are performed for at least one event type, and changing the state of control variables is performed according to said at least one event type.

This permits to modify control variables that are known to be affected by an event, thus increasing the reliability and robustness of event detection.

Advantageously, a plurality of event types is tested, and one instance of steps C) to J) is performed for each event type.

This permits to detect in a single execution of the method a plurality of types of events or anomalies on the network.

Advantageously, the refinement criterion comprises the calculation of values of one of a least square and a Bayesian objective function, and the selection and modification of control variables is determined by a Levenberg-Marquardt algorithm.

Using a Bayesian objective function permits to calibrate the relative confidence of the measurements and modified values of control and state variables. Using Levenberg-Marquadt algorithm permits to have good convergence and stability qualities.

Advantageously, classifying an entity of the network is performed by a previously trained machine learning algorithm.

This permits to have rules of classification optimized for a given water distribution system, whose reliability increases over time.

Advantageously, the control variables comprise scalar variables characterizing the topology and the topography of the network, and time-based variables characterizing the inputs and outputs of the network having at least one value at each time reference.

This permits to increase the number of possible events detected by the method.

Advantageously, changing the set of values of the control variables comprises modifying at least one of: the values of a subset of the time-based control; the values of a subset of the scalar control variables calculated during a phase of modeling of the network.

This permits to take full advantage of the availability of scalar and time-based control variables.

Advantageously, modifying the values of a subset of the time-based control variables comprises modifying the values of the time-based control variables representative of water consumption.

This permits to detect events which are detected by an abnormal increase of water consumption, for example leaks.

Advantageously, state variables further characterize pressure.

This permits to have a more complete and efficient modeling of the temporal evolution of the physical parameters of the water distribution system.

The invention also discloses a method of detecting anomalies in a water distribution system composed of a network of nodes, said method comprising: parametrizing a hydraulic model of the water distribution system with a set of values of control variables characterizing the network and its output at the nodes; using sensors on the network to acquire observations of a subset of the state variables at time references; using the hydraulic model to calculate predicted values of a set of state variables characterizing at least a water velocity at the nodes, said values having time references; computing residue values of the subset of the state variables as a difference between predicted values and observations at the time references; performing a statistical analysis of residue values at an entity of the network at a selection of the time references; classifying the at least one of a node and an arc of the network based on a quality index at the output of the statistical analysis.

Advantageously, the statistical analysis comprises the calculation of one of a mean, a standard deviation, an absolute mean or median deviation, an auto-regressive model, boxplot, Support Vector Machine, a Principal Component Analysis (PCA), K-nearest neighbor and results of statistical tests (computing P-values).

This permits a better understanding of the historical behavior of the water distribution system, and an adaptation of the detection to new situation with the feedback of users.

Advantageously, a node is classified in a class representative of an abnormal state if the residue values at the entity of the network exceeds a threshold based on statistical analysis of historical data and operational risk level a predefined number of successive time references.

Advantageously, a method comprising the step of the two methods described above further comprises classifying the network in a state at the output of: J) Classifying an entity of the network in a state according to the set of control variables, and; classifying the at least one of a node and an arc of the network based on a quality index at the output of the statistical analysis.

The invention also discloses a system for detecting anomalies in a water distribution system composed of a network of nodes, said system comprising: sensors of at least water velocity and pressure at a subset of nodes of the network; a computing device comprising a processor; communication links between sensors and the computing device; a storage media; wherein the computing device is configured for: retrieving an initial set of values of control variables characterizing the network and its output at the nodes from the storage media and using it to parametrize a hydraulic model of the water distribution system; using communication links between sensors on the network to acquire observations of a subset of the state variables, said observations having time references; executing one of the method disclosed above.

The invention also discloses a computer program product, stored on a non-transitory computer-readable medium, detecting anomalies in a water distribution system composed of a network of nodes, said computer program product comprising code instructions for executing one of the methods disclosed above.

The invention improves the reliability of detection and identification of abnormal states in a water distribution network.

The invention is applicable to a wide range of anomalies, comprising detection of leaks, degradation of water quality, issue with sensor, and incorrect behavior of device of the drinking water distribution system.

Anomalies detected by the method and confirmed by operators can be used to feed learning algorithms in order to detect further anomalies with an increased accuracy.

The method has an improved robustness thanks to the use of historical measurements. The method takes into account the observed variability and uncertainty of the measurements.

The method eases localization and identification of anomalies, and is able to present anomalies to an operator sorted by criticality.

The method reduces the cost of operating a water distribution system, by limiting the number of human interventions on the system.

The method reduces the cost of operating a water distribution system, by limiting the number of sensors required for precisely locating anomalies.

All embodiments described in the present specification may be combined in any combination, except mutually exclusive combinations.

