RESEARCH PAPER
Temperature Prediction for Electromechanical Equipment in Open-Pit Coal Mines Under Complex Working Conditions Using Wavelet Packet Decomposition and Graph Attention Network
1
Xinjiang University, China
2
Xinjiang Tianchi Energy Co., Ltd., China
Submission date: 2024-05-27
Final revision date: 2024-10-04
Acceptance date: 2024-12-14
Online publication date: 2024-12-19
Publication date: 2024-12-19
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(3):197381
HIGHLIGHTS
- The prediction model combines wavelet packet decomposition and graph attention network.
- Long-term and short-term features obtained by WPD of temperature series data.
- GAT can automatically capture features and assign the appropriate weights.
- The feasibility of the model is verified using belt conveyor motor temperature data.
KEYWORDS
Temperature predictionWavelet packet decompositionGraph attention networkOpen-pit coal mineComplex working conditions
TOPICS
ABSTRACT
The electromechanical equipment in open-pit coal mines is influenced by perturbing factors such as load, ambient temperature, and frequent startups and shutdowns, which result in low accuracy and poor generalization performance of the prediction model for its operating state. This paper considers both long-term temperature fluctuations and episodic changes caused by these perturbing factors. Additionally, multidimensional time and spatial data are integrated to propose a temperature prediction model for mining electromechanical equipment based on wavelet packet decomposition and a graph attention network (WPD-GAT). The temperature data of electromechanical equipment in an open pit coal mine in Xinjiang are used for experimental verification and comparison with four models. The experimental results show that the model proposed has an improvement in prediction performance, indicating the superiority of the model in the prediction of the temperature of electromechanical equipment of open-pit coal mines under complex working conditions.
ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation of China (No. 72361032), and Technology Innovation Program for Doctoral Students (XJU2022BS091).
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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