@inproceedings{5119ecde1f54426cb1508c4c214ffe71,
title = "Fault Diagnosis of Wind Motor based on Convolutional Neural Network",
abstract = "This paper studies the fault diagnosis of wind motors, which is an important way to improve the safety and reliability of wind motors. It is non-trivial to extract the fault features from the original vibration signals by the traditional methods. We propose a novel method to improve the fault diagnosis performances of wind motors. First, the Wigner-Ville distribution method is used to generate the time-frequency images of the vibration signals in different speed ranges of the motor, which is helpful for fault features extraction. Then, we use the convolutional neural network, an important tool in the field of deep learning, to extract the fault features from the time-frequency images. Finally, simulation results based on the measurement data of an actual wind motor are provided to demonstrate the effectiveness of the proposed method.",
keywords = "convolutional neural network, deep learning, fault detection, time-frequency, wind motor",
author = "Xinyang Liu and Yakun Wang and Hui Yin and Zhenhua Wang and Yi Shen",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020 ; Conference date: 20-11-2020 Through 22-11-2020",
year = "2020",
month = nov,
day = "20",
doi = "10.1109/DDCLS49620.2020.9275079",
language = "英语",
series = "Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1404--1409",
editor = "Mingxuan Sun and Huaguang Zhang",
booktitle = "Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020",
address = "美国",
}