@inproceedings{9cd62e8f834740f2836ac6c5aa64f563,
title = "Mechanical Fault Diagnosis of Circuit Breaker Based on Autoencoder Neural Network and Support Vector Machine",
abstract = "Mechanical faults is one of the main faults that occur in the circuit breaker. The vibration signal generated during the opening and closing process of the circuit breaker can effectively reflect its operating state. In this paper, the vibration signal of the circuit breaker under normal and fault conditions is collected by the self made online monitor of the circuit breaker, and the vibration signal is analyzed and processed by using autoencoder neural network and support vector machine. The experimental results show that the autoencoder neural network can effectively extract the characteristics of the vibration signal of the circuit breaker; the support vector machine is used to diagnose the signal, and the high accuracy is obtained on the experimental samples.",
keywords = "SVM, autoencoder, circuit breaker, fault diagnosis, vibration signal",
author = "Sen Liu and Pengfei Song and Changchun Zhai and Likun Xiong and Fangfei Lei and Yijun Ye and Aijun Yang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 4th IEEE China International Electrical and Energy Conference, CIEEC 2021 ; Conference date: 28-05-2021 Through 30-05-2021",
year = "2021",
month = may,
day = "28",
doi = "10.1109/CIEEC50170.2021.9510390",
language = "英语",
series = "Proceedings of 2021 IEEE 4th International Electrical and Energy Conference, CIEEC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of 2021 IEEE 4th International Electrical and Energy Conference, CIEEC 2021",
}