Prediction of multiple failures for a mobile robot steering system

Fault diagnosis and failure prognosis are critical techniques to improve the safety and reliability of modern complex electromechanical systems. In this paper, a model-based prognosis method is developed to deal with multiple incipient faults in a mobile robot steering system. This method utilizes the concept of Augmented Global Analytical Redundancy Relations (AGARRs) to handle failures with both parametric and non-parametric nature. In order to realize multiple failures prediction, a multiple Hybrid Particle Swarm Optimization (HPSO) algorithm is proposed. Simulation results verify the effectiveness of the proposed method in a front steering system of a CyCab mobile robot.