首页|Fault diagnosis of high voltage circuit breaker based on multi-sensor information fusion with training weights
Fault diagnosis of high voltage circuit breaker based on multi-sensor information fusion with training weights
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NSTL
Elsevier
? 2022 Elsevier LtdTo achieve more accurate identification of mechanical faults for high voltage circuit breaker (HVCB) with higher speed, multi-sensor information fusion has been proposed in this paper. The wavelet packet decomposition is used to decompose the signals collected by various sensors. Then, the energy of the wavelet packets in different frequency band can be obtained to constitute eigenvectors. After that, Dempster/Shafer (D-S) evidence theory can be applied for fault identification, where neural networks are built to train the weight of sensors without prior information. The conflicts existing in information fusion can be solved and the accuracy of fault diagnosis is improved. Finally, experimental validation is carried out to show the effectiveness of the proposed fault diagnosis strategy for the HVCB.
D-S evidence theoryFault diagnosisHVCBMulti-sensor information fusionNeural network
Zhang J.、Wu Y.、Xu Z.、Din Z.、Chen H.
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School of Electrical Engineering Southeast University
State Grid Jiangsu Electric Power Co. Ltd Maintenance Branch
NSTL
Elsevier
