@inproceedings{8abe206616c5412fb901436978761f57,
title = "Fault diagnosis of hydraulic actuator based on improved convolutional neural network",
abstract = "This paper proposes a fault diagnosis approach for hydraulic actuator based on short-time Fourier transform and convolutional neural network. The common failure modes of hydraulic actuator include external leakage, internal leakage and crawling, while it is difficult to measure and diagnose above failures with traditional fault diagnosis method. This paper focuses on the signal variance of pressure of rodless chamber of actuator, extract the effective fault features with Short-Time Fourier Transform (STFT) and use convolutional neural network to carry out the fault diagnosis of the leakage and crawling of actuator with time-frequency image. Simulation results show that the proposed method has good accuracy in distinguishing classic failures under different operating conditions.",
keywords = "component, convolutional neural network, fault diagnosis, hydraulic actuator, short-time Fourier transform, time-frequency image",
author = "Liwei Zhao and Shaoping Wang and Jian Shi and Chao Zhang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020 ; Conference date: 20-08-2020 Through 23-08-2020",
year = "2020",
month = aug,
doi = "10.1109/APARM49247.2020.9209471",
language = "英语",
series = "2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020",
address = "美国",
}