Abstract
Rolling bearing is a kind of precision mechanical component bearing that changes the sliding friction between the running shaft and the shaft seat into rolling friction, so as to reduce the friction loss. It is a very important part of mechanical equipment, and its life prediction is of great significance. In this regard, a residual life prediction method based on bi-directional Gate recurrent unit is proposed. Firstly, the time domain, frequency domain and time-frequency domain features are screened, and three evaluation indexes are defined to quantitatively evaluate the effect of feature parameters characterizing the bearing degradation process. The sensitive feature set is screened. A bi-directional Gate recurrent unit network is built, and the sensitive feature set is used as input, The normalized single point life value is changed to segment life value as a label to train the neural network, and finally the life prediction of rolling bearing is realized. Finally, it is verified on the public data set that this method can accurately predict the remaining life of rolling bearings.
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Acknowledgments
This research was funded by the Bei**g Natural Science Foundation (Grant no. L211010, 3212032), and National Railway Administration (Grant no. AJ2021-043). The authors wish to extend their sincere thanks for the support from the Bei**g Municipal Science & Technology Commission of China.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Gong, Z. et al. (2023). Life Prediction of Rolling Bearing Based on Bidirectional GRU. In: Cao, W., Hu, C., Chen, X. (eds) Proceedings of the 3rd International Symposium on New Energy and Electrical Technology. ISNEET 2022. Lecture Notes in Electrical Engineering, vol 1017. Springer, Singapore. https://doi.org/10.1007/978-981-99-0553-9_17
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DOI: https://doi.org/10.1007/978-981-99-0553-9_17
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