| Issue |
E3S Web Conf.
Volume 261, 2021
2021 7th International Conference on Energy Materials and Environment Engineering (ICEMEE 2021)
|
|
|---|---|---|
| Article Number | 01039 | |
| Number of page(s) | 4 | |
| Section | Energy Development and Energy Storage Technology Research and Development | |
| DOI | https://doi.org/10.1051/e3sconf/202126101039 | |
| Published online | 21 May 2021 | |
Research on Life Prediction of Fan Spindle Bearing Based on Gated Recurrent Unit Neural Network
Shanghai DianJi University, Shanghai, 201306, China
* Corresponding author: 1430487268@qq.com
In order to solve the problems of redundancy calculation and inefficiency of traditional machine learning algorithm in dealing with large amount of historical data of fan, a new predictive algorithm based on gated recurrent unit (GRU) is proposed to predict the remaining service life of fan spindle bearing. Firstly, the vibration history data of the main shaft bearing of the fan is analyzed to find out the relationship between the characteristic value and the remaining life, and the characteristic parameters which can reflect the remaining life are selected; Then, GRU is used to build the remaining service life prediction model of spindle bearing, and the main parameters of the model are adjusted to improve the prediction accuracy of the model. Compared with long short term (LSTM) algorithm, GRU is an effective tool to deal with a large number of data sets.
© The Authors, published by EDP Sciences, 2021
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