Mechanical Condition Monitoring of Vacuum Circuit Breakers Using Artificial Neural Network

Yongpeng MENG; Shenli JIA; Mingzhe RONG

doi:10.1093/ietele/e88-c.8.1652

Mechanical Condition Monitoring of Vacuum Circuit Breakers Using Artificial Neural Network

Yongpeng MENG , Shenli JIA, Mingzhe RONG

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Using the Vibration signatures obtained during the operations as the original data, a mechanical condition monitoring method for vacuum circuit breaker is developed in this paper. The method combined the time-frequency analysis and the condition recognition based on artificial neural network. During preprocessing, the vibration signature was decomposed into individual frequency bands using the arithmetic of wavelet packets. The signal energy in the main frequency bands was used to form the condition feature vector, which was input to the artificial neural network for condition recognition. By introducing the parameter of approximation degree, a new recognition arithmetic based on Radial Basis Function was constructed. This approach could not only distinguish these conditions that belong to different known condition modes but also distinguish new condition modes.

Publication: IEICE TRANSACTIONS on Electronics Vol.E88-C No.8 pp.1652-1658

Publication Date: 2005/08/01

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DOI: 10.1093/ietele/e88-c.8.1652

Type of Manuscript: Special Section PAPER (Special Section on Recent Development of Electro-Mechanical Devices--Selected Papers from International Session on Electro-Mechanical Devices 2004 (IS-EMD2004)--)

Category: Contactors & Circuit Breakers

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Yongpeng MENG , Shenli JIA, Mingzhe RONG, "Mechanical Condition Monitoring of Vacuum Circuit Breakers Using Artificial Neural Network" in IEICE TRANSACTIONS on Electronics, vol. E88-C, no. 8, pp. 1652-1658, August 2005, doi: 10.1093/ietele/e88-c.8.1652.
Abstract: Using the Vibration signatures obtained during the operations as the original data, a mechanical condition monitoring method for vacuum circuit breaker is developed in this paper. The method combined the time-frequency analysis and the condition recognition based on artificial neural network. During preprocessing, the vibration signature was decomposed into individual frequency bands using the arithmetic of wavelet packets. The signal energy in the main frequency bands was used to form the condition feature vector, which was input to the artificial neural network for condition recognition. By introducing the parameter of approximation degree, a new recognition arithmetic based on Radial Basis Function was constructed. This approach could not only distinguish these conditions that belong to different known condition modes but also distinguish new condition modes.
URL: https://global.ieice.org/en_transactions/electronics/10.1093/ietele/e88-c.8.1652/_p

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@ARTICLE{e88-c_8_1652,
author={Yongpeng MENG , Shenli JIA, Mingzhe RONG, },
journal={IEICE TRANSACTIONS on Electronics},
title={Mechanical Condition Monitoring of Vacuum Circuit Breakers Using Artificial Neural Network},
year={2005},
volume={E88-C},
number={8},
pages={1652-1658},
abstract={Using the Vibration signatures obtained during the operations as the original data, a mechanical condition monitoring method for vacuum circuit breaker is developed in this paper. The method combined the time-frequency analysis and the condition recognition based on artificial neural network. During preprocessing, the vibration signature was decomposed into individual frequency bands using the arithmetic of wavelet packets. The signal energy in the main frequency bands was used to form the condition feature vector, which was input to the artificial neural network for condition recognition. By introducing the parameter of approximation degree, a new recognition arithmetic based on Radial Basis Function was constructed. This approach could not only distinguish these conditions that belong to different known condition modes but also distinguish new condition modes.},
keywords={},
doi={10.1093/ietele/e88-c.8.1652},
ISSN={},
month={August},}

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TY - JOUR
TI - Mechanical Condition Monitoring of Vacuum Circuit Breakers Using Artificial Neural Network
T2 - IEICE TRANSACTIONS on Electronics
SP - 1652
EP - 1658
AU - Yongpeng MENG
AU - Shenli JIA
AU - Mingzhe RONG
PY - 2005
DO - 10.1093/ietele/e88-c.8.1652
JO - IEICE TRANSACTIONS on Electronics
SN -
VL - E88-C
IS - 8
JA - IEICE TRANSACTIONS on Electronics
Y1 - August 2005
AB - Using the Vibration signatures obtained during the operations as the original data, a mechanical condition monitoring method for vacuum circuit breaker is developed in this paper. The method combined the time-frequency analysis and the condition recognition based on artificial neural network. During preprocessing, the vibration signature was decomposed into individual frequency bands using the arithmetic of wavelet packets. The signal energy in the main frequency bands was used to form the condition feature vector, which was input to the artificial neural network for condition recognition. By introducing the parameter of approximation degree, a new recognition arithmetic based on Radial Basis Function was constructed. This approach could not only distinguish these conditions that belong to different known condition modes but also distinguish new condition modes.
ER -