Research on high frequency fault prediction of elevator based on combined model

Zuodong Liang; Zhiqiang Guo

doi:10.1117/12.3004011

11 October 2023 Research on high frequency fault prediction of elevator based on combined model

Zuodong Liang, Zhiqiang Guo

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128005N (2023) https://doi.org/10.1117/12.3004011
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

The safety hazards caused by elevator faults are increasing day by day, and it is urgent to study the problem of elevator fault prediction. In this paper, a combined model elevator fault prediction method based on CNN, LSTM and self-attentive mechanism is proposed. The operating parameters and high frequency faults of elevators are statistically determined, and a large amount of real-time elevator data is collected to build a data set. The advanced features are extracted by CNN and fed to LSTM for training, and then fed to softmax classifier for classification prediction after further feature extraction by the self-attention mechanism. The experimental results show that the method can effectively predict elevator faults.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zuodong Liang and Zhiqiang Guo "Research on high frequency fault prediction of elevator based on combined model", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128005N (11 October 2023); https://doi.org/10.1117/12.3004011

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