Leak Detection and Isolation in Pressurized Water Pipe Networks using Interval LPV Models

Postprint (published version

On the role of virtual laboratories in an undergraduate power electronics introductory course

Preprin

Hybrid Automaton Incremental Construction for Online Diagnosis

This paper proposes a method to track the system mode and diagnose a hybrid system without building an entire diagnoser off-line. The method is supported by a hybrid automaton model that represents the hybrid system continuous and discrete behavioral dynamics. Diagnosis is performed by interpreting the events and measurements issued by the physical system directly on the hybrid automaton model. This interpretation leads to building the useful parts of the diagnoser incrementally, developing only the branches that are required to explain the occurrence of the incoming events. The resulting diagnoser adapts to the system operational life and is much less demanding in terms of memory storage. The proposed framework subsumes previous works in that it copes with both structural and non-structural faults. The method is validated on an application case study based on the sewer network of the Barcelona city.Peer ReviewedPostprint (author’s final draft

Leak Detection, Isolation and Estimation in Pressurized Water Pipe Networks using LPV Models and Zonotopes

In this paper, a leak detection, isolation and estimation methodology in pressurized water pipe networks is proposed. The methodology is based on computing residuals which are obtained comparing measured pressures (heads) in selected points of the network with their estimated values by means of a Linear Parameter Varying (LPV) model and zonotopes. The structure of the LPV model is obtained from the non-linear mathematical model of the network. The proposed detection method takes into account modelling uncertainty using zonotopes. The isolation and estimation task employs an algorithm based on the residual fault sensitivity analysis. Finally, a typical water pipe network is employed to validate the proposed methodology. This network is simulated using EPANET software. Parameters of LPV model and their uncertainty bounded by zonotopes are estimated from data coming from this simulator. A leak scenario allows to assess the effectiveness of the proposed approach.Preprin

A methodology for building a fault diagnoser for hybrid systems

In this paper, a design methodology for building diagnosers for hybrid systems is proposed. The design methodology uses as a starting point a hybrid automaton model to represent the hybrid system behaviour by means of the interaction of continuous dynamics and discrete events. Then, a hybrid fault diagnoser is designed using the methodology described in this paper and implemented by means of a discrete event system which carries out the mode recognition and diagnostic tasks, both based on residuals generated using models. Both tasks interact each other since the diagnosis module adapts according to the current mode of the hybrid system. The mode recognition task involves detecting and identifying the mode change by determining the set of residuals that are consistent with the current mode of the hybrid system. On the other hand, the diagnostic task involves detecting and isolating faults by identifying the fault that can explain the set of residuals that are inconsistent. A section of the Barcelona sewer network is used as application case study to illustrate the proposed fault diagnosis for hybrid systems.Peer ReviewedPostprint (author’s final draft

Set membership parity space hybrid system diagnosis

In this paper, diagnosis for hybrid systems using a parity space approach that considers model uncertainty is proposed. The hybrid diagnoser is composed of modules which carry out the mode recognition and diagnosis tasks interacting each other, since the diagnosis module adapts accordingly to the current hybrid system mode. Moreover, the methodology takes into account the unknown but bounded uncertainty in parameters and additive errors using a passive robust strategy based on the set-membership approach. An adaptive threshold that bounds the effect of model uncertainty in residuals is generated for residual evaluation using zonotopes, and the parity space approach is used to design a set of residuals for each mode. The proposed fault diagnosis approach for hybrid systems is illustrated on a piece of the Barcelona sewer network.Postprint (author's final draft

Leak Detection and Isolation in Pressurized Water Pipe Networks using Interval LPV Models

Abstract: In this paper, a leak detection and isolation methodology in pressurized water pipe networks is proposed. The methodology is based on computing residuals which are obtained comparing measured pressures (heads) in selected points of the network and their estimated values by means of an interval Linear Parameter Varying (LPV) model. The structure of the LPV models is obtained from the non-linear mathematical model of the network. The proposed detection method uses interval LPV models to obtain uncertainty intervals for the estimated heads that allow to indicate when a leak appears in the water network. The isolation task employs an algorithm based on the residual fault sensitivity analysis. Finally, a typical water pipe network is employed to validate the proposed methodology. This network is simulated using EPANET software. Parameters of LPV models and their uncertainty bounded by intervals are estimated from data coming from this simulator. Several leak scenarios allow to assess the effectiveness of the proposed approach

A methodology for building a fault diagnoser for hybrid systems

In this paper, a design methodology for building diagnosers for hybrid systems is proposed. The design methodology uses as a starting point a hybrid automaton model to represent the hybrid system behaviour by means of the interaction of continuous dynamics and discrete events. Then, a hybrid fault diagnoser is designed using the methodology described in this paper and implemented by means of a discrete event system which carries out the mode recognition and diagnostic tasks, both based on residuals generated using models. Both tasks interact each other since the diagnosis module adapts according to the current mode of the hybrid system. The mode recognition task involves detecting and identifying the mode change by determining the set of residuals that are consistent with the current mode of the hybrid system. On the other hand, the diagnostic task involves detecting and isolating faults by identifying the fault that can explain the set of residuals that are inconsistent. A section of the Barcelona sewer network is used as application case study to illustrate the proposed fault diagnosis for hybrid systems.Peer Reviewe

Parity space hybrid system diagnosis under model uncertainty

In this paper, diagnosis for hybrid systems using a parity space approach that considers model uncertainty is proposed. The hybrid diagnoser is composed of modules which carry out the mode recognition and diagnosis tasks both based on residuals generated using a model. Both tasks interact each other since the diagnosis module adapts himself according to the current mode of the hybrid system. Moreover, the methodology takes into account the parameter uncertainty using a passive robust strategy. An adaptive threshold for residual evaluation is generated and the parity space approach is used to design a set of residuals for each mode. The proposed fault diagnosis approach for hybrid systems is illustrated on a part of the Barcelona sewer network.Peer Reviewe