Abstract

Trabajo presentado al 19th IFAC World Congress celebrado del 24 al 29 de agosto de 2014 en Cape Town (Sudafrica). In recent years, inland navigation networks benefit from the innovation of the instrumentation and SCADA systems. These data acquisition and control systems lead to a reactive asset-management of inland navigation networks. However, sensors and actuators are subject to faults due to the strong effects of the environment, aging, etc. In this paper, a sensor Fault Detection and Isolation (FDI) approach is proposed using an Integrator-Delay-Zero (IDZ) model, interval observers and the dynamic classification algorithm AUDyC. The combined use of these approaches allows the improvement of the sensor fault diagnosis. The proposed approach is introduced through the case study of the Cuinchy-Fontinettes reach in the north of France. This work is a contribution to the GEPET’Eau project which is granted by the French ministery MEDDE - GICC, the French institution ORNERC and the DGITM Peer Reviewed

Abstract

Inland navigation networks are equipped with limnimeters to measure and record water level data for the control of water levels and the management of water resources. When faults occur on sensors, corrupted data can be considered as correct, leading to undesirable management actions. Therefore, it is necessary to detect and localize these faults. In this paper, the detection and localization of sensor faults is performed through the analysis of the parameters of a grey-box model, which are obtained from available real data. The parameters are determined with a sliding window, with the exception of the delays, which are considered known a priori. A fault is detected and then localized when there is a change in the value of the parameters. This approach is well suited for constant faults and particularly well adapted for intermittent faults. Data of an inland navigation reach located in the north of France are used to highlight the performance of the proposed approach. This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the projects DEOCS (ref. MINECO DPI2016- 76493), SCAV (ref. MINECO DPI2017-88403-R) and through the grant IJCI-2014-20801. This work has also been partially funded by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482). Peer reviewed

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