Abstract
As high-voltage electric equipment has complex structure and works in harsh environment, this paper is aimed at applying Optical Fiber Sensors to a temperature-variation fault diagnosis system of high-voltage electric equipment based on the combination of neural network and expert system. Neural network has the characteristics of self-adapted, distributed storage and associative memory. Using BP neural network, we can make on-line diagnosis of temperature-variation fault of high-voltage electric equipment. All the uses above can increase the speed of diagnosis and make results be more exact.
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© 2007 Springer Berlin Heidelberg
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Wang, ZY., Li, YW., Guo, P., Yu, XF. (2007). Temperature-Variation Fault Diagnosis of the High-Voltage Electric Equipment Based on the BP Neural Network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_78
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DOI: https://doi.org/10.1007/978-3-540-72395-0_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
eBook Packages: Computer ScienceComputer Science (R0)