摘要：An appropriate threshold is the key factor in a diagnosis of fault. However, the traditional method of setting a fixed threshold does not take into consideration the influence of system status and noise interference, and it often leads to false alarms and missed detections of system fault. To improve the accuracy of fault diagnosis, we first obtained the residual signal based on the strong tracking filter method – cubature Kalman filtering. We then proposed an adaptive dynamic threshold adjustment algorithm based on the grey theory. In this method, the threshold value can be dynamically adjusted according to the real-time mean and variance of the residual. Finally, we performed a sensor fault experiment involving three sensors in different locations of a robot. The results demonstrate the feasibility of our proposed method.
