title: Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory.
abstract: Sensor data fusion plays an important role in fault diagnosis. Dempster-Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing 400715, China. yuankaijuan@163.com.
authors.forename: Kaijuan
authors.initials: K
authors.lastname: Yuan
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing 400715, China. xiaofuyuan@swu.edu.cn.
authors.forename: Fuyuan
authors.initials: F
authors.lastname: Xiao
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing 400715, China. feiliguoswu@163.com.
authors.forename: Liguo
authors.initials: L
authors.lastname: Fei
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing 400715, China. kangbingyiswu@163.com.
authors.forename: Bingyi
authors.initials: B
authors.lastname: Kang
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing 400715, China. ydeng@swu.edu.cn.|School of Electronic and Information, Northwestern Polytechnical University, Xi'an 710072, China. ydeng@swu.edu.cn.|Big Data Decision Institute, Jinan University, Tianhe, Guangzhou 510632, China. ydeng@swu.edu.cn.|Department of Civil & Environmental Engineering, School of Engineering, Vanderbilt University, Nashville, TN 37235, USA. ydeng@swu.edu.cn.
authors.forename: Yong
authors.initials: Y
authors.lastname: Deng
keywords: Dempster–Shafer evidence theory; Deng entropy; belief function; evidential conflict; fault diagnosis; sensor data fusion; sensor reliability