Research on Prediction of Mechanical Vibration Trends of Hydroelectric Units Based on Improved Deep Belief Network

Aiming at the problem that traditional models cannot accurately predict, a method for predicting the vibration trend of hydropower units based on an improved deep belief network is proposed. Firstly, we can use variational mode decomposition to process vibration signals. Then, feature extraction is performed on the processed signal, and a sparrow search algorithm is used to improve the diagnostic model. Finally, a multi-step prediction model combining variational mode decomposition and deep belief network was proposed to achieve the prediction of vibration trends. The results indicate that the prediction model has high accuracy, good prediction performance, and rich reference value.

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Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1109/ceect63656.2024.10898799
Primary Topic
Advanced Algorithms and Applications
Type
article
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article

Research on Prediction of Mechanical Vibration Trends of Hydroelectric Units Based on Improved Deep Belief Network

Haoran Zhao, J. Y. Liu, Jiaze Qin, Peng Wang et al.
Advanced Algorithms and Applications
article

Research on Prediction of Mechanical Vibration Trends of Hydroelectric Units Based on Improved Deep Belief Network

Haoran Zhao, J. Y. Liu, Jiaze Qin, Peng Wang, Jianan Yang, Chen Ding, Lv Jiarui
article en

Abstract

Aiming at the problem that traditional models cannot accurately predict, a method for predicting the vibration trend of hydropower units based on an improved deep belief network is proposed. Firstly, we can use variational mode decomposition to process vibration signals. Then, feature extraction is performed on the processed signal, and a sparrow search algorithm is used to improve the diagnostic model. Finally, a multi-step prediction model combining variational mode decomposition and deep belief network was proposed to achieve the prediction of vibration trends. The results indicate that the prediction model has high accuracy, good prediction performance, and rich reference value.

Shandong University (CN), University of Hong Kong (HK)
Openalex Percentile: Top 65%
Advanced Algorithms and Applications
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