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Article

Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

1
School of Computer and Information Science, Southwest University, Chongqing 400715, China
2
School of Electronic and Information, Northwestern Polytechnical University, Xi’an 710072, China
3
Big Data Decision Institute, Jinan University, Tianhe, Guangzhou 510632, China
4
Department of Civil & Environmental Engineering, School of Engineering, Vanderbilt University, Nashville, TN 37235, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Sensors 2016, 16(1), 113; https://doi.org/10.3390/s16010113
Received: 29 November 2015 / Revised: 3 January 2016 / Accepted: 11 January 2016 / Published: 18 January 2016
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: sensor data fusion; sensor reliability; Dempster–Shafer evidence theory; belief function; Deng entropy; fault diagnosis; evidential conflict sensor data fusion; sensor reliability; Dempster–Shafer evidence theory; belief function; Deng entropy; fault diagnosis; evidential conflict
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MDPI and ACS Style

Yuan, K.; Xiao, F.; Fei, L.; Kang, B.; Deng, Y. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory. Sensors 2016, 16, 113. https://doi.org/10.3390/s16010113

AMA Style

Yuan K, Xiao F, Fei L, Kang B, Deng Y. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory. Sensors. 2016; 16(1):113. https://doi.org/10.3390/s16010113

Chicago/Turabian Style

Yuan, Kaijuan, Fuyuan Xiao, Liguo Fei, Bingyi Kang, and Yong Deng. 2016. "Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory" Sensors 16, no. 1: 113. https://doi.org/10.3390/s16010113

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