Mechanical Condition Monitoring of Vacuum Circuit Breakers Using Artificial Neural Network(Session 5 : Contactactors & Circuit Breakers)
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概要
- 論文の詳細を見る
The vibration signature collected on the framework of the vacuum circuit breaker is decomposed into individual frequency bands using the algorithm of wavelet packets. Main signal energy in these bands is extracted to form the condition eigenvectors. For specific condition classification, the concept of credibility is introduced in this paper. A new recognition algorithm based on the Radial Basis Function (RBF) neural network is proposed and applied to the monitoring of vacuum circuit breakers. Multiple experiments under different conditions verify that the new algorithm can recognize not only the known conditions but also a new condition of the breakers.
- 社団法人電子情報通信学会の論文
- 2004-10-15
著者
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JIA Shenli
State Key Laboratory of Electrical Insulation and Power Equipment, Department of Electrical Engineer
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Jia S
State Key Laboratory Of Electrical Insulation For Power Equipment Xi'an Jiaotong University
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Jia Shenli
State Key Lab Of Electrical Insulation For Power Equipment Xi'an Jiaotong University
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RONG Mingzhe
State Key Lab of Electrical Insulation and Power Equipment, Xi'an Jiaotong University
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Rong Mingzhe
State Key Lab Of Electrical Insulating And Power Equipment Xi'an Jiaotong University
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Rong Mingzhe
State Key Laboratory Of Electrical Insulation For Power Equipment Xi'an Jiaotong University
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Meng Yongpeng
State Key Laboratory of Electrical Insulation for Power Equipment, Xi'an Jiaotong University
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Meng Yongpeng
State Key Laboratory Of Electrical Insulation For Power Equipment Xi'an Jiaotong University
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