Failure detection of closed-loop systems and application to SI engines

The existing methods of engine fault detection and isolation are based on open-loop control, which are not applicable to closed-loop control systems. In this paper a new fault detection and isolation method for closed-loop control systems is presented. The validity of this method is verified by simulation results. First, the method was tested on the nonlinear simulation of SI engines, the Mean Value Engine Model (MVEM) with different faults was simulated. The neural network based engine air path model was constructed, which was trained with engine input/output data. Then Radial Basis Function (RBF) neural network was used to model the SI engine. The drawback of the training data acquisition was analyzed and a new data acquisition method was proposed, that greatly improved the model accuracy. © 2017, Editorial Board of Jilin University. All right reserved

Analytical Redundancy for Sensor Fault Isolation and Accommodation in Public Transportation Vehicles

The paper discusses an instrument fault detection, isolation, and accommodation procedure for public transportation vehicles. After a brief introduction to the topic, the rule set implementing the procedure with reference to the kinds of sensors usually installed on public transportation vehicles is widely discussed. Particular attention is paid to the description of the rules aimed at allowing the vehicle to continue working regularly even after a sensor fault develops. Finally, both the estimated diagnostic and dynamic performances in the off-line processing of the data acquired in several drive tests are then analyzed and commented upon

Analytical redundancy for sensor fault isolation and accommodation in public transportation vehicles

The paper discusses an instrument fault detection, isolation, and accommodation procedure for public transportation vehicles. After a brief introduction to the topic, the rule set implementing the procedure with reference to the kinds of sensors usually installed on public transportation vehicles is widely discussed. Particular attention is paid to the description of the rules aimed at allowing the vehicle to continue working regularly even after a sensor fault develops. Finally, both the estimated diagnostic and dynamic performances in the off-line processing of the data acquired in several drive tests are then analyzed and commented upon

Contribution au diagnostic de pannes pour\ud les systèmes différentiellement plats

Cette thèse s’intéresse au diagnostic de pannes dans les systèmes différentiellement plats, ceci constituant une large classe de systèmes non linéaires. La propriété de platitude différentielle est caractérisée par des relations qui permettent d’exprimer les états d’un système et ses entrées en fonction de ses sorties plates et de leurs dérivées. Ces relations qui sont à la base de la commande plate sont aussi utiles pour la réalisation du diagnostic de pannes. Ainsi sont introduites les notions de minimalité pour les sorties plates, de platitude stricte et de degré additionnel de redondance. Ceci conduit à la proposition d’une méthode globale de détection de pannes basée sur la platitude. Partant alors de la constatation que les systèmes différentiellement plats de complexité élevée sont souvent constituer de sous systèmes eux mêmes différentiellement plats, l’approche de détection de pannes précédente peut être démultipliée au sein de cette structure de façon à en identifier les sous systèmes défaillants. On s’intéresse alors au cas courant de la platitude différentielle implicite et on montre dans le cadre d’une application aéronautique comment les réseaux de neurones permettent de constituer une solution numérique au problème de détection de pannes. La disponibilité en temps réel de dérivées successives des sorties étant essentielle pour la mise en oeuvre de ces méthodes, on étudie alors les performances d’un filtre dérivateur alors que le système est lui-même soumis à une commande plate, ceci conduira a modifié légèrement une telle loi de commande afin d’effectuer l’effet des erreurs d’estimation. On s’intéresse finalement à la détection des pannes dans les systèmes chaotiques différentiellement plats. On montre sur plusieurs exemples comment la propriété de platitude peut être mise à profit pour détecter et identifier des variations paramétriques au sein d’un tel type de système chaotique. Des résultats de simulation sont présentés. Finalement des thèmes de recherche complémentaires à cette approche sont relevés. --------------------------------------------------------------------- This thesis is devoted to the diagnostic of faults in differentially flat systems, where\ud differentially flat systems constitute a rather large class of non linear systems. The flatness\ud property is characterized by relations allowing to express states and input as functions of the\ud outputs and their derivatives up to a finite order. These relations are the basis for the synthesis\ud of flat control laws and are, is it displayed here, useful to perform an efficient diagnostic of\ud additional redundancy degree. Then a global fault detection method based on the flatness\ud property is proposed. It is shown that many differentially flat subsystems so that the proposed\ud fault detection method can be applied within the corresponding structure allowing then the\ud identification of faulty subsystems. Then the frequent case of implicitly differentially flat\ud systems is considered and it is shown through an aeronautical application that neural networks\ud can provide a numerical solution approach to this fault detection problem. Since with this\ud approach the one line availability of successive derivatives of the outputs is imperative, the\ud performance of a derivative filter is studied. To eliminate the effect of the resulting estimation\ud errors, some improvements are introduced to the current flat control law. In the last section of\ud the report the diagnostic of differentially flat chaotic systems is considered. In different cases it is shown how the differential flatness property can be used to detect and identify variations of the parameters of the chaotic system. Simulation results are displayed. Finally some complementary fields of research are pointed out\u