Sensor Fusion in Analogue Circuit Fault Diagnosis Using Transferable Belief Model

DOI

10.2991/iccsee.2013.325How to use a DOI?

Keywords

analog circuit, fault diagnosis, transferable belief model, k-nearest neighbor rule from TBM, fault location, data fusion

Abstract

In fault diagnosis of circuit fault diagnosis system using multi-sensor data fusion may not give reliable fault diagnosis result in cases when processing on inconsistent data carry inconsistent. In order to deal with such problems, This paper has developed an analog-circuit fault diagnostic system based on transferable belief model (TBM) and K-nearest neighbor rule from TBM. The proposed system has the capability to detect and identify fault components in an analog electronic circuit by analyzing its working temperature and testable voltage. Using K-nearest neighbor(KNN) rule to process the working temperature of component drastically enhances the testable information for fault detect, satisfying the need of fusing data from distinct evidence sources. The experimental results show that this system performs significantly better in fault diagnosis of analog circuits due to the proposed techniques.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Youhui Chen
AU  - Dong Cheng
AU  - Yimin Yang
AU  - Zan Xiao
PY  - 2013/03
DA  - 2013/03
TI  - Sensor Fusion in Analogue Circuit Fault Diagnosis Using Transferable Belief Model
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1297
EP  - 1301
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.325
DO  - 10.2991/iccsee.2013.325
ID  - Chen2013/03
ER  -