http://pig.sagepub.com/content/early/2012/01/06/0954410011421717International audienceThis survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic nonlinear dynamical modeling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided

Abstract-All the methods for Fault Detection and Isolation (FDI) involve internal parameters, often called hyperparameters, that have to be carefully tuned. Most often, tuning is ad hoc and this makes it difficult to ensure that any comparison between methods is unbiased. We propose to consider the evaluation of the performance of a method with respect to its hyperparameters as a computer experiment, and to achieve tuning via global optimization based on Kriging and Expected Improvement. This approach is applied to several residualevaluation (or change-detection) algorithms on classical testcases. Simulation results show the interest, practicability and performance of this methodology, which should facilitate the automatic tuning of the hyperparameters of a method and allow a fair comparison of a collection of methods on a given set of test-cases. The computational cost turns out to be much lower than the one obtained with other general-purpose optimization methods such as genetic algorithms.

Various strategies based on differential geometry or system inversion have been proposed to deal with fault detection and isolation (FDI) for nonlinear systems. Many of them require the computation of successive derivatives of inputs and outputs, which might be unrealistic in practical applications where measurements suffer noise and disturbances. In this paper, we take advantage of the fact that, in domains such as aerospace or robotics, sensors allow the measurement of first-order derivatives of state variables. This information, along with the redundancy provided by the control module can be used to generate residuals. Such a procedure is proposed and applied to a generic 2D aeronautical case study.

To cite this version:Julien Marzat, Hélène Piet-Lahanier, Frédéric Damongeot, Eric Walter. Control-based fault detection and isolation for autonomous aircraft. Abstract This paper describes a new method to perform fault detection and isolation for a closedloop-controlled autonomous aircraft. This vehicle is equipped with standard sensors and actuators, and its dynamics is nonlinear. It is assumed that a guidance law and a control loop have been designed to achieve a given mission. The diagnosis method uses the resulting control objectives to generate residuals indicative of the presence of faults. Two classical guidance laws are considered, leading to different control constraints and diagnosis signals. A structural sensitivity analysis shows that all sensor and actuator faults can be detected and all sensor faults isolated, for both laws. The fault diagnosis procedure does not require the costly integration of the model of the system, and the closed-loop scheme makes it robust to model uncertainty. Realistic simulation results with strong model and measurement uncertainty demonstrate the potential of the approach. A theoretical analogy with observer-based fault diagnosis is also derived.