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ABSTRACT:In this work, a model based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals, indicators that are obtained comparing measured inputs and outputs with analytical relationships, which are obtained by system modelling. The innovation of this methodology is based on the characterization of the relative residual fault sensitivity. To illustrate the results, a non-linear fuel cell simulator proposed in the literature is used, with modifications, to include a set of fault scenarios proposed in this work. Finally, it is presented the diagnosis results corresponding to these fault scenarios. It is remarkable that with this methodology it is possible to diagnose and isolate all the faults in the proposed set in contrast with other well known methodologies which use the binary signature matrix of analytical residuals and faults.

Abstract-This paper proposes a model-based fault diagnosis approach for wind turbines and its application to a realistic wind turbine fault diagnosis benchmark. The proposed fault diagnosis approach combines the use of analytical redundancy relations (ARRs) and interval observers. Interval observers consider an unknown but bounded description of the model parametric uncertainty and noise using the the so-called set-membership approach. This approach leads to formulate the fault detection test by means of checking if the measurements fall inside the estimated output interval, obtained from the mathematical model of the wind turbine and noise/parameter uncertainty bounds. Fault isolation is based on considering a set of ARRs obtained from structural analysis of the wind turbine model and a fault signature matrix that considers the relation of ARRs and faults. The proposed fault diagnosis approach has been validated on a 5MW wind turbine using the NREL FAST simulator. The obtained results are presented and compared with other approaches proposed in the literature.Index Terms-Analytical redundant relations, interval-based observers, model-based fault diagnosis, wind turbines.

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In this paper, the ''passive approach'' to robust fault detection and isolation (FDI) is presented in the context of observer methodology, when a model with parameters bounded in intervals (''interval model'') is used, deriving the interval version corresponding to the classical use of observers. The passive approach is based on allowing the effect of the uncertainties to propagate into the residuals and then the principle of adaptive thresholds is used to achieve robustness. Finally, the approach proposed is applied to detect some of the faults proposed in an industrial actuator used as an FDI benchmark in the European RTN DAMADICS. r

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This paper proposes a fault diagnosis method using a timed discrete-event approach based on interval observers which improves the integration of fault detection and isolation tasks. The interface between fault detection and fault isolation considers the activation degree and the occurrence time instant of the diagnostic signals using a combination of several theoretical fault signature matrices which store the knowledge of the relationship between diagnostic signals and faults. The fault isolation module is implemented using a timed discrete event approach that recognizes the occurrence of a fault by identifying a unique sequence of observable events (fault signals). The states and transitions that characterize such a system can be inferred directly from the relation between fault signals and faults. The proposed fault diagnosis approach has been motivated by the problem of detecting and isolating faults of the Barcelona's urban sewer system limnimeters (level meter sensors). The results obtained in this case study illustrate the benefits of using the proposed approach in comparison with the standard fault detection and isolation approach.

This study presents a solution for the integrated longitudinal and lateral control problem of urban autonomous vehicles. It is based on a gain-scheduling linear parameter-varying (LPV) control approach combined with the use of an Unknown Input Observer (UIO) for estimating the vehicle states and friction force. Two gain-scheduling LPV controllers are used in cascade configuration that use the kinematic and dynamic vehicle models and the friction and observed states provided by the Unknown Input Observer (UIO). The LPV-UIO is designed in an optimal manner by solving a set of linear matrix inequalities (LMIs). On the other hand, the design of the kinematic and dynamic controllers lead to solve separately two LPV-Linear Quadratic Regulator problems formulated also in LMI form. The UIO allows to improve the control response in disturbance affected scenarios by estimating and compensating the friction force. The proposed scheme has been integrated with a trajectory generation module and tested in a simulated scenario. A comparative study is also presented considering the cases that the friction force estimation is used or not to show its usefulness.