@inproceedings{61f933ee81594beeb59ba2edb83fc030,
title = "A Deep Ensemble Learning Model for Rolling Bearing Fault Diagnosis",
abstract = "Rolling bearing is an important component of rotating machinery. The accurate fault diagnosis of rolling bearing is very important. Nowadays, experts began to explore the combination strategies of deep learning networks. Ensemble learning can achieve higher recognition accuracy by combining multiple models. Therefore, a deep ensemble learning model is proposed for rolling bearing fault diagnosis. Firstly, four different Convolutional Neural Networks (CNN) networks are constructed as the base-learners. Secondly, the 4-fold cross validation method is adopted for training the base-learner. Finally, the Artificial Neural Network (ANN) is used as the meta-learner and the stacking method is used for model ensemble. The proposed method can get high classification accuracy and accurately identify all kinds of faults.",
keywords = "ensemble learning, fault diagnosis, rolling bearing, stacking",
author = "Ruixin Wang and Hongkai Jiang and Zhenning Li and Yunpeng Liu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Prognostics and Health Management, ICPHM 2022 ; Conference date: 06-06-2022 Through 08-06-2022",
year = "2022",
doi = "10.1109/ICPHM53196.2022.9815733",
language = "英语",
series = "2022 IEEE International Conference on Prognostics and Health Management, ICPHM 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "133--136",
booktitle = "2022 IEEE International Conference on Prognostics and Health Management, ICPHM 2022",
}