期刊
MEASUREMENT
卷 175, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2021.109166
关键词
Roller bearing; One dimensional convolution; Simple recurrent unit; Remaining useful life
资金
- National Natural Science Foundation of China [51975038]
- Nature Science Foundation of Beijing, China [L191005]
- Support plan for the development of high-level teachers in Beijing municipal universities [CITTCD201904062, CITTCD201704052]
- General Project of Scientific Research Program of Beijing Education Commission [KM201810016015]
- Scientific Research Fund of Beijing University of Civil Engineering Architecture [ZF15068]
- BUCEA Post Graduate Innovation Project [PG2020088]
- Fundamental Research Funds for Beijing Universities [X20071]
- Fundamental Research Funds for Beijing University of Civil Engineering and Architecture [X18133]
This study proposed a novel method for predicting the remaining useful life of roller bearings, which combines 1D-CNN and SRU networks. It can reduce manual intervention and time cost to a certain extent while improving prediction accuracy, providing guidance for intelligent prediction of roller bearing remaining useful life.
To overcome the shortcomings of traditional roller bearing remaining useful life prediction methods, which mainly focus on prediction accuracy improvement and ignore labor cost and time, the present work proposed a novel prediction method by combining an improved one-dimensional convolution neural network (1D-CNN) and a simple recurrent unit (SRU) network. For feature extraction, the proposed method uses the ability of the 1DCNN to extract signal features. Moreover, use the global maximum pooling layer to replace the fully connected layer. In the prediction part, a parallel-input SRU network was established by reconstructing the serial operation mode of a traditional recurring neural network (RNN). Finally, experiments were carried out using the XJTU-SY dataset to verify. Results revealed that on the premise of ensuring prediction accuracy, the 1D-CNN-SRU method could reduce manual intervention and time cost to a certain extent and provide an intelligent method for roller bearing remaining useful life prediction.
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