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.
Highlights
Fault diagnosis is widely used in real life
A new method of engine fault diagnosis based on sensor data fusion is presented
Feature matrix and diagnosis matrix are constructed with regard to focal elements in the power set
Summary
Fault diagnosis is widely used in real life. It is investigated and applied to many areas such as electrical motors,[1] analog circuits,[2] and dynamic systems.[3]. A method of engine fault diagnosis based on sensor data fusion is proposed. It should be noted that the conflict in evidence theory is an open issue.[49] Many methods have been proposed to address this issue.[42,43,50] Another issue is how to handle dependent evidence combination in real application, which greatly affect data fusion result.[51,52]. An evidential fault diagnosis A new method to represent the multiple hypotheses Assuming N faults of an engine are taken into consideration, the frame of discernment is constructed as. Assuming L diagnosis vectors is measured, a diagnosis matrix is constructed to represent the engine’s real state.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
