Abstract
With the rapid development of urban rail transit in recent years, it becomes necessary to ensure the operation safety of train wheelset axle boxes. Aiming at the problems of large model size and long diagnosis time in traditional fault diagnosis methods, this paper proposed a novel model to identify concurrent faults in wheelset axle boxes based on an efficient neural network and the attention mechanism. The model was developed based on an improved Ghost bottleneck module sequence to achieve an efficient model. Furthermore, the use of the convolutional block attention module adaptively refined the feature map to improve the generalization of the model. Feature pyramid network was used to fuse the shallow and deep features in the network to improve the extraction ability of various size features. Experiments were carried out on wheelset axle box concurrent fault datasets. Model size, diagnosis speed and accuracy were used as evaluation indexes to compare the efficiencies of existing fault diagnosis methods and our proposed model. Experimental results revealed that the proposed algorithm effectively diagnosed wheelset axle box concurrent faults.
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Acknowledgements
The present work was funded by the National Natural Science Foundation of China (Grant No. 51975038), the Nature Science Foundation of Beijing, China (Grant No. L191005), the Support plan for the development of high-level teachers in Beijing municipal universities (Grant Nos. CIT&TCD201904062 and CIT&TCD201704052), the General Project of Scientific Research Program of Beijing Education Commission Grant No. (SQKM201810016015), the Scientific Research Fund of Beijing University of Civil Engineering Architecture (Grant No. ZF15068), the BUCEA Post Graduate Innovation Project (Grant No. PG2020088) and the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (Grant No. X18133).
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Yao, D., Liu, H., Yang, J. et al. Implementation of a novel algorithm of wheelset and axle box concurrent fault identification based on an efficient neural network with the attention mechanism. J Intell Manuf 32, 729–743 (2021). https://doi.org/10.1007/s10845-020-01701-y
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DOI: https://doi.org/10.1007/s10845-020-01701-y