A multi-model based fault detection and diagnosis of internal sensors for mobile robot

A multi-model based approach to fault detection and diagnosis (FDD) of internal sensor for mobile robot is proposed. Three failure modes (hard failure mode, noise failure mode, and scale failure mode) of the sensor are handled; on the hard failure the sensor output is stuck at a constant value. The noise failure causes the sensor output with large noise. On the scale failure the gain (scale) of the sensor output differs from the normal. The detection and diagnosis of the hard/noise failure (HNFDD) is based on the variable structure interacting multiple-model (VSIMM) estimator; changes of the failure modes are modeled as switching from one mode to another in a probabilistic manner. The mode probabilities, which are estimated based on a bank of Kalman filters, provide the fault decision. The detection and diagnosis of the scale failure (SFDD) is achieved via single-model based Kalman filters, each of which is based on a model matching to a failure mode of particular sensors; the fault decision is made by comparing the model conditional estimates in the sensor gain. The proposed FDD algorithm is implemented on our skid-steered mobile robot with five internal sensors (four wheel-encoders and one yaw-rate gyro). Experimental results show the property of the FDD algorithm.