Abstract

Parallel transmission lines are difficult to protect due to mutual coupling between circuits. This paper proposes a Naïve Bayes classifier (NBC) based fault detection and classification technique for protection of parallel transmission line involving inter-circuit faults. NBC is a good classification tool for larger data sets as the training process takes less time with greater accuracy. Input given to the fault detection module is the fundamental components of three phase current signals of both circuits. Input given to the fault phase identification and fault classification module is the fundamental component of three phase current signals and zero sequence currents of both the circuits. Seven separate classifiers are designed for fault phase identification for A1, B1, C1, A2, B2, C2 and G. From fault phase identification module faults are classified. Accuracy of the proposed method is 100% for fault detection and 99.99% for classification of fault from all the tested fault cases. Response time of the proposed method is within 10 ms for all the fault cases studied.

Abstract

International audience; This paper deals with sensors faults detection and isolation using method by observers. The principle is to reconstruct the state vector and measurements of the system by sliding mode observers and compare the estimated outputs with those measured. In this work, a multi observer's technique is used. It consists on construction of many observers, at least one observer for each output (sensor measurement). Each observer must be robust to noises and to other uncertainties but sensitive to sensor faults. The residual is the estimation error witch is the difference between sensor measurement and its estimated. Without failure in the sensor, this residual remains around zero and if a fault is occurred, it deviates significantly from zero. This method is applied to an autonomous electric vehicle called RobuCar. Simulation results are given at the end to show the effectiveness of the approach.

Abstract

Dependable complex systems often operate under variable and non-stationary conditions, which requires efficient and extensive monitoring and error detection solutions. Among the many, the paper focuses on anomaly detection techniques, which monitor the evolution of some specific indicators through time to identify anomalies, i.e. deviations from the expected operational behavior. The timely identification of anomalies in dependable, fault tolerant systems allows to timely detect errors in the services and react appropriately. In this paper, we investigate the possibility to monitor the evolution of indicators through time using the random walk model on indicators belonging to Operating Systems, specifically in our study the Linux Red Hat EL5. The approach is based on the experimental evaluation of a large set of heterogeneous indicators, which are acquired under different operating conditions, both in terms of workload and faultload, on an air traffic management target system. The statistical analysis is based on a best-fitting approach aiming to minimize the integral distance between the empirical data distribution and some reference distributions. The outcomes of the analysis show that the idea of adopting a random walk model for the development of an anomaly detection monitor for critical systems that operates at Operating System level is promising. Moreover, standard distributions such as Laplace and Cauchy, rather than Normal, should be used for setting up the thresholds of the monitor. Further studies that involve a new application, a different Operating System and a new layer (an Application Server) will allow verifying the generalization of the approach to other fault tolerant systems, monitored layers and set of indicators.

Abstract

Los controladores MPC son buenos eliminando el error de estado estacionario aun si el sistema controlado es afectado por perturbaciones. Pero el MPC no fue pensado para mantener su desempeño bajo condiciones de falla por sí solo. El control tolerante a fallas se compone de algoritmos de control, estrategias de detección y compensación de fallas. En este trabajo se implementó un controlador MPC usando la estrategia de matriz dinámica junto con un generador de residuo basado en modelo para detectar la ocurrencia de una falla en el sistema controlado. La cuantificación de fallas se logró mediante el uso de Redes neuronales artificiales y un filtro pasa banda, lo cual permitió realizar una acción de compensación para mejorar el tiempo de recuperación del sistema ante una falla.

Abstract

6 pages; International audience; The paper proposes a control system with the brake and the steering for developing a driver assistance system. The purpose is to design a cruise control method to track the road geometry with a predefined velocity and guarantee the road stability of the vehicle simultaneously. An actuator selection method is developed in the control design, in which the actuator limits, energy requirements and vehicle operations are taken into consideration. The method is extended with a fault-tolerant feature based on a robust LPV method, into which the actuator selection procedure and the detected fault information are incorporated. The operation of the reconfigurable control system is illustrated through various vehicle manoeuvres.