@inproceedings{6f718eac7a0c4597b8b752104bfc8a21,
title = "SMOTE-Based Fault Diagnosis Method for Unbalanced Samples",
abstract = "Industrial processes are changing with each passing day, and the probability of failure is also increasing, and accurate fault diagnosis is becoming extremely important. In this paper, SMOTE-based fault diagnosis method for unbalanced samples is proposed. First, the SMOTE algorithm is used to oversample the unbalanced sample. Second, considering the high dimensionality of industrial data, the FDA algorithm is used for feature extraction. Third, the AdaBoost algorithm is used for fault diagnosis. Finally, the simulation validation is performed on the TFF dataset. The method proposed in this paper has higher diagnostic accuracy than other methods.",
keywords = "AdaBoost, FDA, Fault Diagnosis, SMOTE, Unbalanced Samples",
author = "Yuan Xu and Xiaoqian Cheng and Wei Ke and Zhu, {Qun Xiong} and He, {Yan Lin} and Yang Zhang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022 ; Conference date: 03-08-2022 Through 05-08-2022",
year = "2022",
doi = "10.1109/DDCLS55054.2022.9858365",
language = "English",
series = "Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "682--686",
editor = "Mingxuan Sun and Zengqiang Chen",
booktitle = "Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022",
address = "United States",
}