SMOTE-Based Fault Diagnosis Method for Unbalanced Samples

Yuan Xu, Xiaoqian Cheng, Wei Ke, Qun Xiong Zhu, Yan Lin He, Yang Zhang

研究成果: Conference contribution同行評審

摘要

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.

原文English
主出版物標題Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022
編輯Mingxuan Sun, Zengqiang Chen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面682-686
頁數5
ISBN(電子)9781665496759
DOIs
出版狀態Published - 2022
事件11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022 - Emeishan, China
持續時間: 3 8月 20225 8月 2022

出版系列

名字Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022

Conference

Conference11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022
國家/地區China
城市Emeishan
期間3/08/225/08/22

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