Abstract

Part 7: Control and Fault Tolerance; International audience; This study presents a scheme to detect and isolate faults in over-actuated electric vehicles. Although this research work is still emerging, it already provides a view of the main challenges on the problem and discusses some possible approaches that can be useful to overcome the key difficulties. This paper intends to present a fault detection algorithm based on Unknown Input Observer (UIO). The residuals are built through the difference of signals between the measured outputs and the output estimations from the observer. The main idea is to detect fault in the electric motors and steering wheel actuator. The algorithm is presented and tested with some fault scenarios using the co-simulation tool between Simulink/MATLAB and the high-fidelity model from Carsim software. Document type: Part of book or chapter of book

Abstract

This article regards the development of an analytical redundancy-based approach for detecting and isolating both sensor and actuator faults in flat inland navigation canals. Inland navigation networks are principally used for transport and are composed of many canalized natural rivers and artificial canals characterized by no slope. These canals are strongly affected by resonance phenomena, which can create waves such that the navigation condition might not be guaranteed. It is, therefore, required to ensure dealing with fault-free measured data and actuators. The proposed approach is based on the integrator delay zero model of the flat inland navigation canal. The proposed method is tested by considering the Cuinchy–Fontinettes navigation reach (in the north of France) to detect and isolate the occurrence of faults in the Cuinchy and Fontinettes level sensors and in the Cuinchy gate. This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECOP) and FEDER through the project HARCRICS (ref. DPI2014-58104- R) and through the grant IJCI-2014-20801. Peer reviewed

Abstract

Inland navigation networks are mainly used for transport with economic and environmental benefits. In a climate change context which leads to the scarcity of the water resource, the control of navigation levels and the supervision of these networks become crucial. Thus, this paper is focused on the sensors Fault Detection and Isolation of inland navigation reaches. A modeling method based on the identification technique is proposed. Then, based on residuals, the dynamic classification algorithm AUDyC leads to the detection and diagnosis of sensors faults. Setting errors and slow drifts are considered. The proposed methods are applied on the Cuinchy- Fontinettes reach benchmark. Peer Reviewed Document type: Conference object

Abstract

Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have also been actively studied in recent years as functions that assist drivers to a higher level. These DASs (driver assistance systems) are implemented using vehicle sensors that observe vehicle status and send signals to the ECU (electronic control unit). Therefore, the failure of each system sensor affects the function of the system, which not only causes discomfort to the driver but also increases the risk of accidents. In this paper, we propose a new method to detect and isolate faults in a vehicle control system. The proposed method calculates the constraints and residuals of 12 systems by applying the model-based fault diagnosis method to the sensor of the chassis system. To solve the inaccuracy in detecting and isolating sensor failure, we applied residual sensitivity to a threshold that determines whether faults occur. Moreover, we applied a sensitivity analysis to the parameters semi-correlation table to derive a fault isolation table. To validate the FDI (fault detection and isolation) algorithm developed in this study, fault signals were injected and verified in the HILS (hardware-in-the-loop simulation) environment using an RCP (rapid control prototyping) device. Document type: Article