In this paper, a new approach for sensor placement in water distribution networks (WDN) is proposed. The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the non-linear integer and large-scale nature of the resulting optimization problem, genetic algorithms (GA) are used as solution approach. To validate the results obtained, they are compared with exhaustive search methods with higher computational cost proving that GA allow to find near-optimal solutions with less computational load. The proposed sensor placement algorithm is combined with a projection-based isolation scheme. However, the proposed methodology does not depend on the isolation method chosen by the user and it could be easily adapted to any other isolation scheme. Experiments on a real network allow to evaluate the performance of such approach.

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Optimal sensor placement for leak location in water distribution networks using genetic algorithms

Sensor placement for fault location identification in water networks

When discrete-event systems are used to model systems with a large number of possible (reachable) states, many problems such as simulation, optimization, and control, may become computationally prohibitive because they require some enumeration of such states. A common way to effectively address this issue is fluidization. The goal of this paper is that of studying the effect of fluidization on fault diagnosis. In particular, we focus on the purely logic Petri net (PN) model that results in the untimed continuous PN model after fluidization. In accordance to most of the literature on discrete-event systems, we define three diagnosis states, namely N, U , and F, corresponding respectively to no fault, uncertain, and fault state. We prove that, given an observation, the resulting diagnosis state can be computed solving linear programming problems rather than integer programming problems as in the discrete case. The main advantage of fluidization is that it enables to deal with much more general PN structures. In particular, the unobservable subnet needs not be acyclic as in the discrete case. Moreover, the compact representation of the set of consistent markings using convex polytopes can be seen in some cases as an improvement in terms of computational complexity.

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Fault Diagnosis of Discrete-Event Systems Using Continuous Petri Nets

Selecting residual generators for detecting and isolating faults in a system is an important step when designing model-based diagnosis systems. However, finding a suitable set of residual generators to fulfill performance requirements is complicated by model uncertainties and measurement noise that have negative impact on fault detection performance. The main contribution is an algorithm for residual selection that combines model-based and data-driven methods to find a set of residual generators that maximizes fault detection and isolation performance. Based on the solution from the residual selection algorithm, a generalized diagnosis system design is proposed where test quantities are designed using multivariate residual information to improve detection performance. To illustrate the usefulness of the proposed residual selection algorithm, it is applied to find a set of residual generators to monitor the air path through an internal combustion engine.

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A Combined Data-Driven and Model-Based Residual Selection Algorithm for Fault Detection and Isolation

The performance of a leak detection and location algorithm depends on the set of measurements that are available in the network. This work presents an optimization strategy that maximizes the leak diagnosability performance of the network. The goal is to characterize and determine a sensor configuration that guarantees a maximum degree of diagnosability while the sensor configuration cost satisfies a budgetary constraint. To efficiently handle the complexity of the distribution network an efficient branch and bound search strategy based on a structural model is used. However, in order to reduce even more the size and the complexity of the problem the present work proposes to combine this methodology with clustering techniques. The strategy developed in this work is successfully applied to determine the optimal set of pressure sensors that should be installed to a District Metered Area in the Barcelona Water Distribution Network.

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Sensor placement for leak detection and location in water distribution networks

The success of any diagnosis strategy critically depends on the sensors measuring process variables. This paper presents a strategy based on diagnosability maximization for optimally locating sensors in distribution networks. The goal is to characterize and determine the set of sensors that guarantees a maximum degree of diagnosability taking into account a given sensor configuration cardinality constraint. The strategy is based on the structural model of the system under consideration. Structural analysis is a powerful tool for determining diagnosis possibilities and evaluating whether the number and the location of sensors are adequate in order to meet some diagnosis specifications. The proposed approach is successfully applied to leakage detection in a Drinking Water Distribution Network.

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Sensor placement for fault diagnosis performance maximization in Distribution Networks

This paper focuses on residual generation for model-based fault diagnosis. Specifically, a methodology to derive residual generators when nonlinear equations are present in the model is developed. A main result is the characterization of computation sequences that are particularly easy to implement as residual generators and that take causal information into account. An efficient algorithm, based on the model structure only, which finds all such computation sequences, is derived. Furthermore, fault detectability and isolability performances depend on the sensor configuration. Therefore, another contribution is an algorithm, also based on the model structure, that places sensors with respect to the class of residual generators that take causal information into account. The algorithms are evaluated on a complex highly nonlinear model of a fuel cell stack system. A number of residual generators that are, by construction, easy to implement are computed and provide full diagnosability performance predicted by the model.

/pdf/fault-diagnosis-based-on-causal-computations-d2uxakgorz.pdf

Fault Diagnosis Based on Causal Computations

This work is devoted to find an optimal set of sensors for model-based FDI. The novelty is that binary integer linear programming is used in the optimization problem, leading to a formulation of the detectability and isolability specifications as linear inequality constraints. Furthermore, a very detailed system model is not needed since the methodology handles structural models. The approach has been successfully applied to a two-tank system, as an illustrative example.

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Optimal Sensor Placement for FDI using Binary Integer Linear Programming

This paper presents the application of a new methodology for Fault Detection and Isolation (FDI) to a Fuel Cell System. The work is devoted to find an optimal set of sensors for model-based FDI. The novelty is that binary integer linear programming (BILP) is used in the optimization formulation, leading to a reformulation of the detectability and isolability specifications as linear inequality constraints. The approach has been successfully applied to a Fuel Cell System.

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Optimal sensor placement For Fuel Cell System diagnosis using BILP formulation

https://orbilu.uni.lu/bitstream/10993/4492/1/WeC1-04.pdf

Albert Rosich

Papers

Sensor placement for fault diagnosis performance maximization in Distribution Networks

Fault Diagnosis Based on Causal Computations

Optimal Sensor Placement for FDI using Binary Integer Linear Programming

Optimal sensor placement For Fuel Cell System diagnosis using BILP formulation

Sensor Placement for Fault Diagnosis Based on Causal Computations