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.
In case of model uncertainty is located in parameters (interval model), an interval observer has been shown to be a suitable strategy to generate an adaptive threshold to be used in residual evaluation. In interval observerbased fault detection methods, the observer gain plays an important role since it determines the minimum detectable fault for a given type of fault and allows enhancing the observer fault detection properties while diminishing model computational drawbacks (i.e. Wrapping effect, Computational complexity). In this paper, the effect of the observer gain on the time evolution of the residual sensitivity to a fault is analyzed. Then, using these sensitivity studies, the minimum detectable fault time evolution is established. Thus, three types of faults according their detectability time evolution are introduced: permanently (strongly) detected, non-permanently (weakly) detected or just non-detected. Finally, an example based on a mineral grindingclassification process will be used to illustrate the results derived.
Abstract-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.
Efficient and reliable operation of Polymer Electrolyte Membrane (PEM) fuel cells are key requirements for their successful commercialization and application. The use of diagnostic techniques enables the achievement of these requirements. This paper focuses on model-based Fault Detection and Isolation (FDI) for PEM fuel cell stack systems. The work consists in designing and selecting a subset of consistency relations such that a set of predefined faults can be detected and isolated. Despite a nonlinear model of the PEM fuel cell stack system will be used, consistency relations that are easily implemented by a variable back substitution method will be selected. The paper also shows the significance of structural models to solve diagnosis issues in complex systems.
Abstract-This work focuses on residual generation for modelbased fault diagnosis. Specifically, a methodology to derive residual generators when non-linear equations are present in the model is developed. A main result is the characterization of computational 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, that finds all such computational sequences, is derived. Further, fault detectability and fault isolability performance depend on the sensor configuration. Therefore, another contribution is an algorithm, also based on 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 non-linear, model of a fuel cell stack system. A number of residual generators are computed that are, by construction, easy to implement and provide full diagnosability performance predicted by the model. Index Terms-Fault diagnosis, causal computations, sensor placement, fuel cell stack system.
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