Sensor fault detection in autonomous mining trucks with unknown varying gross weight

Abstract

This paper focuses on the problem of the sensor fault detection in Autonomous Mining Trucks (AMTs) with the unknown varying gross weight and measurement noise. The dust and extreme temperatures in strip mines can lead to bias and drift faults in the sensors of AMTs. Besides, due to the bumpy roads in mining areas, the longevity and accuracy of the weight sensors cannot be guaranteed, which makes the weight sensor useless for AMTs. Therefore, the gross weight is treated as an unknown parameter in the lateral dynamics model of AMTs. The emphasis or difficulty lies in obtaining the state estimation and reducing the false alarm rate under the unknown variations in gross weight. This paper proposes an interval observer with the zonotope method to estimate the AMT state under the condition of unknown variations in gross weight. An interval residual generator with the generalized likelihood ratio test and the zonotope method is proposed for the sensor fault detection in AMTs. Finally, the effectiveness of the proposed approach is validated through the simulations of an AMT.