Showing papers by "Ramon Sarrate published in 2007"

Optimal sensor placement for model-based fault detection and isolation

Abstract: 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

Abstract: 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.

Observer gain effect in linear interval observer-based fault detection

Abstract: 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 observer-based 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 grinding- classification process will be used to illustrate the results derived.

Fault-tolerant explicit MPC of PEM fuel cells

Abstract: 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.