An adaptive threshold algorithm for sensor fault based on the grey theory

Lifeng Wu,Beibei Yao,Zhen Peng,Yong Guan

doi:10.1177/1687814017693193

Abstract

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.

Highlights

Sensors are being applied in a wide range of areas, both civil and military
In contrast to data-driven techniques, which require a great deal of data and are of low accuracy, fault diagnosis algorithms based on models are developed to monitor the consistency between the measured outputs of the sensors and the predicted outputs of the model
We compared the use of the adaptive threshold selection method with the fixed threshold method in the robot’s sensor fault detection

Summary

Introduction

Sensors are being applied in a wide range of areas, both civil and military. The sensor is the most critical component in systems of measurement and control, but it is prone to failure, leading to system paralysis. These methods require the building of an accurate mathematical model of the sensor based on control theory and statistical analysis, which will yield a comparative analysis of the residual and the threshold to determine whether a fault has occurred. An online fault threshold adjustment algorithm based on grey theory is proposed in this article, where the mean of the residual and the variance are used as the parameters to dynamically adjust the threshold and update the threshold in real-time.

Results

Conclusion