Next Article in Journal
Decoupling Principle Analysis and Development of a Parallel Three-Dimensional Force Sensor
Next Article in Special Issue
Efficient Data Gathering Methods in Wireless Sensor Networks Using GBTR Matrix Completion
Previous Article in Journal
Using Crowdsourced Trajectories for Automated OSM Data Entry Approach
Previous Article in Special Issue
Spiking Cortical Model Based Multimodal Medical Image Fusion by Combining Entropy Information with Weber Local Descriptor
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(9), 1509; doi:10.3390/s16091509

Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, Shanxi, China
*
Author to whom correspondence should be addressed.
Academic Editors: Xue-Bo Jin, Feng-Bao Yang, Shuli Sun and Hong Wei
Received: 22 July 2016 / Revised: 9 September 2016 / Accepted: 12 September 2016 / Published: 15 September 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
View Full-Text   |   Download PDF [1113 KB, uploaded 15 September 2016]   |  

Abstract

Sensor data fusion technology is widely employed in fault diagnosis. The information in a sensor data fusion system is characterized by not only fuzziness, but also partial reliability. Uncertain information of sensors, including randomness, fuzziness, etc., has been extensively studied recently. However, the reliability of a sensor is often overlooked or cannot be analyzed adequately. A Z-number, Z = (A, B), can represent the fuzziness and the reliability of information simultaneously, where the first component A represents a fuzzy restriction on the values of uncertain variables and the second component B is a measure of the reliability of A. In order to model and process the uncertainties in a sensor data fusion system reasonably, in this paper, a novel method combining the Z-number and Dempster–Shafer (D-S) evidence theory is proposed, where the Z-number is used to model the fuzziness and reliability of the sensor data and the D-S evidence theory is used to fuse the uncertain information of Z-numbers. The main advantages of the proposed method are that it provides a more robust measure of reliability to the sensor data, and the complementary information of multi-sensors reduces the uncertainty of the fault recognition, thus enhancing the reliability of fault detection. View Full-Text
Keywords: sensor data fusion; Z-number; fault diagnosis; fuzzy; Dempster–Shafer evidence theory; BPA; uncertainty sensor data fusion; Z-number; fault diagnosis; fuzzy; Dempster–Shafer evidence theory; BPA; uncertainty
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jiang, W.; Xie, C.; Zhuang, M.; Shou, Y.; Tang, Y. Sensor Data Fusion with Z-Numbers and Its Application in Fault Diagnosis. Sensors 2016, 16, 1509.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top