DATA FUSION ALGORITHM OF FAULT DIAGNOSIS CONSIDERING SENSOR MEASUREMENT UNCERTAINTY

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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VOLUME 6 , ISSUE 1 (February 2013) > List of articles

DATA FUSION ALGORITHM OF FAULT DIAGNOSIS CONSIDERING SENSOR MEASUREMENT UNCERTAINTY

Xu Xiaobin * / Zhou Zhe * / Wen Chenglin *

Keywords : sensor data fusion, fault diagnosis, random-fuzzy variable, similarity measure, Dempster-Shafer evidence theory.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 6, Issue 1, Pages 171-190, DOI: https://doi.org/10.21307/ijssis-2017-534

License : (CC BY-NC-ND 4.0)

Received Date : 30-August-2012 / Accepted: 11-January-2013 / Published Online: 20-February-2013

ARTICLE

ABSTRACT

This paper presents data fusion algorithm of fault diagnosis considering sensor measurement uncertainty. Random-fuzzy variables (RFV) are used to model testing patterns (TPs) and fault template patterns (FTPs) respectively according to on-line sensor monitoring data and typical historical sensor data reflecting every fault mode. A similarity measure is given to calculate matching degree between a TP and each FTP in fault database such that Basic Probability Assignment (BPA) can be obtained by normalizing matching degree. Several BPAs provided by many sensor sources are fused by Dempster’s rule of combination. A diagnosis decision-making can be done according to the fusion results. Finally,the diagnosis examples of machine rotor system with vibration sensors show that the proposed method can enhance accuracy and reliability of data fusion-based diagnosis system.

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REFERENCES

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