Ramon Sarrate

Leak Localization in Water Networks: A Model-Based Methodology Using Pressure Sensors Applied to a Real Network in Barcelona [Applications of Control]

The efficient distribution of water is a subject of major concern for water utilities and authorities [1]. While some leaks in water distribution networks (WDNs) are unavoidable, one of the main challenges in improving the efficiency of drinking water networks is to minimize leaks. Leaks can cause significant economic losses in fluid transportation and extra costs for the final consumer due to the waste of energy and chemicals in water treatment plants. Leaks may also damage infrastructure and cause third-party damage and health risks. In many WDNs, losses due to leakage are estimated to account up to 30% of the total amount of extracted water [2]; a very important issue in a world struggling to satisfy water demands of a growing population.

Optimal sensor placement for model-based fault detection and isolation

The problem of optimal sensor placement for FDI consists in determining the set of sensors that minimizes a pre-defined cost function satisfying at the same time a pre- established set of FDI specifications for a given set of faults. The main contribution of this paper is to propose an algorithm for model-based FDI sensor placement based on formulating a mixed integer optimization problem. FDI specifications are translated into constraints of the optimization problem considering that the whole set of ARRs has been generated, under the assumption that all candidate sensors are installed. To show the effectiveness of this approach, an application based on a two-tanks system is proposed.

Efficient optimal sensor placement for model-based FDI using an incremental algorithm

The problem of optimal sensor placement for FDI consists in determining the set of sensors that minimizes a pre-defined cost function satisfying at the same time a pre-established set of FDI specifications for a given set of faults. Existing approaches are mainly based on formulating an optimization problem once the sets of all possible ARRs has been generated, considering all possible candidate sensors installed. However, the associated computational complexity is exponential with the number of possible sensors. The main goal of this paper is to propose an incremental algorithm for FDI sensor placement that tries to avoid the computational burden. To show the effectiveness of this approach, an application based on a fuel-cell system is proposed.

Sensor placement for leak detection and location in water distribution networks

The performance of a leak detection and location algorithm depends on the set of measurements that are available in the network. This works presents and 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 disnosability while the sensor configuration cost satifies a budgetary constraint. To efficiently handle the complexity of the distribuion networl an efficient branch and bound search strategy based on a strucutrual 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 Baarcelona Water Distribution Network.

Fault Diagnosis Based on Causal Computations

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.

Fault detection and isolation of hybrid system using diagnosers that combine discrete and continuous dynamics

In this paper, a design methodology and implementation architecture for diagnosers in the framework of hybrid systems is proposed. The design methodology is based on the hybrid automata model that represents the system behaviour by means of the interaction of continuous dynamics and discrete events. The architecture is composed by means of modules which realize the mode recognition and diagnostic tasks both based on residuals generated using models. Both tasks interact each other since the diagnosis module adapts accordingly to the current mode of the hybrid system. The mode recognition task involves detecting the mode change and identification 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 the fault by identifying the fault that can explain the set of residuals that are observed as inconsistent. As an application case study to illustrate the proposed fault diagnosis for hybrid systems a piece of the Barcelona sewer network is used.

Wastewater treatment process supervision by means of a fuzzy automaton model

Describes a methodology for the design of a supervisory system applied to a wastewater treatment process. A behavioural model is built by the joint participation of the process expert along with clustering techniques applied to measured signals. A fuzzy automaton becomes an heuristic model of the process under supervision. In the application example real data for an activated sludge wastewater plant was used. The automaton states are identified by the expert as significant operation situations and the transitions are generated from input measured variables. Thus, this model reflects the dynamics of the continuous system underneath.

Sensor placement for fault diagnosis performance maximization in 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.

Optimal sensor placement For Fuel Cell System diagnosis using BILP formulation

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.

Fault-tolerant explicit MPC of PEM fuel cells

In this paper, fault-tolerant explicit MPC control of fuel cell systems is presented. MPC is one of the control methodologies that allows to introduce fault-tolerance more easily. Here, this capability is extended using recent explicit MPC control theory. Explicit MPC control allows to derive offline the control without using optimization. Moreover, it allows to introduce as additional parameters faults since it is based on parametric programming. This makes possible to change in real-time controller parameters without recomputing the MPC controller or having a bank of pre-computed MPC controllers. Finally, the proposed approach is assessed on a known test bench PEM fuel cell system.