An equipment fault diagnosis method based on unbalanced industrial big data

Yi He; Xueyan Li; Hao Xu; Tao Zhao; Yuxin Han

doi:10.1117/12.2674931

2 May 2023 An equipment fault diagnosis method based on unbalanced industrial big data

Yi He, Xueyan Li, Hao Xu, Tao Zhao, Yuxin Han

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 1264214 (2023) https://doi.org/10.1117/12.2674931
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

With the development of the industrial big data, research on data-driven industrial equipment fault diagnosis has received more and more attention. However, in the process of data acquisition, the equipment failure frequency is low, and the data set becomes serious imbalance. In order to solve this problem, we propose an unbalanced data generated method based on GAN. And in order to improve the accuracy of equipment fault diagnosis, we propose a fault diagnosis method based on CNN-LSTM, which can effectively utilize the spatial and temporal characteristics of data.

Citation Download Citation

Yi He, Xueyan Li, Hao Xu, Tao Zhao, and Yuxin Han "An equipment fault diagnosis method based on unbalanced industrial big data", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 1264214 (2 May 2023); https://doi.org/10.1117/12.2674931

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