A Combined Logistic and Model Based Approach for Fault Detection and Identification in a Climbing Robot

Abstract

This paper presents a combined logistic and model based approach for fault detection and identification (FDI) in the suction foot control of a climbing robot. For this control system, some fault models are easily given by kinematics equations. Moreover, the logic relations of the system states have been known in advance. Based on the combination of the logic reasoning and the model based estimation, the novel approach is properly fit for the FDI application to the climbing robot. First a fault tree (FT) constructed from the target system is used in robot safety analysis by evaluating the basic events (elementary causes) which can lead to a root event (a particular fault). Then, the multiple-model adaptive estimation (MMAE) algorithm is used to reliably detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the MMAE, other faults are detected and identified using the logic reasoning. Experimental results validated that the faults of the sensors and actuators in the suction foot control of the robot can be readily detected and identified by this approach.