Design of Sensor Permanent Fail-safe Algorithm based on Deep Learning for 100Nm class Electro-Hydraulic Power Steering System of Medium and Heavy Commercial Vehicle

In this paper, Deep Learning-based sensor Fail-safe for Electro-Hydraulic Steering Systems (EHPS) of 100Nm commercial vehicle is introduced. There are four types of permanent faults, such as shortage, drifted sensitivity, drifted off-set and fixation. When steering wheel angle sensor and motor angular velocity sensor in steering system failed, permanent faults are recovered by using a Deep Learning-based sensor Fail-safe algorithm. The learning variables are given by Electro-hydraulic motor modeling of steering system and are trained by using Tensor Flow. The learning data are extracted through MATLAB/Simulink and TruckSim. The proposed algorithm is verified through Co-simulation of MATLAB/Simulink and TruckSim. Finally, this paper confirmed that the algorithm proposed recovered the faults of steering system sensors.