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.
Highlights
The vehicle control system improves the performance of the braking, steering, and suspension.ESC, which is a vehicle chassis control system, is used to maintain the driving stability in consideration of the driving situation of the driver, the vehicle condition, and the road conditions [1,2]
4,reported the calculated residual to calculate the this paper, we propose a new method of fault diagnosis, sensitivity-based fault detection, and sensitivity to the fault signal
We used constraints based on vehicle dynamics to diagnose faults in sensors used in We used constraints based on vehicle dynamics diagnose faults in sensors the used in automobiles
Summary
The vehicle control system improves the performance of the braking, steering, and suspension. A randomized failure detection method was proposed using a generalized canonical correlation analysis without the use of a Gaussian model [22] Another groundbreaking study was conducted to diagnose. A study was conducted on an dynamic observer using real-time fuzzy calculations to diagnose faults on systems without sensors using only models [23]. With the significant improvement in computational conducted on systems where the accurate determination and separation of failures are critical, with power and efficiency of computers, research was conducted to detect and classify faults using many types of sensors operating simultaneously, asfailure is thediagnosis case in automobiles. Research has performedyet based on vehicle dynamics tothe diagnose faults on sensors in theofvehicle andcritical, verified using to be conducted on systems where accurate determination and separation failures are Carsim, a with vehicle simulator.
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