Engine fault diagnosis based on sensor data fusion using evidence theory

Abstract

Evidence theory is widely used in fault diagnosis due to its efficiency to model and fuse sensor data. However, one shortcoming of the existing evidential fault diagnosis methods is that only the basic probability assignments in singletons can be generated. In this article, a new evidential fault diagnosis method based on sensor data fusion is proposed. Feature matrix and diagnosis matrix are constructed by sensor data. A discrimination degree is defined to measure the difference between the sensor reports and feature vector. The basic probability assignment of each sensor report can be determined by the proposed discrimination degree. One merit of the proposed method is that not only singletons but also multiple hypotheses are considered. The final diagnosis result is obtained by the combination of several sensor reports. A practical fault diagnosis application is illustrated to show the efficiency of the proposed method.