Abstract
Bearings are one of the core components used in the mechanical equipment. However, mechanical failures caused by rolling bearing failures account for around 20%–40%. The convolutional neural network model is effective at detecting the fault of rolling bearings, but it suffers overfitting problem. In this paper, we propose a combined network model for the fault detection of rolling bearings by combining the convolutional neural network model and the random forest algorithm. Experiment results show that the combined network model can achieve the expected results in the classification accuracy of rolling bearing mechanical faults.
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Acknowledgement
The authors acknowledge the Key Research and Development Plan of Anhui Province (202104d07020006), the Natural Science Foundation of Anhui Province (2108085MF223), University Natural Sciences Research Project of Anhui Province (KJ2021A0991), the Key Research and Development Plan of Hefei (2021GJ030).
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Chen, T., Zhang, G., Wu, T. (2023). Fault Detection of Rolling Bearings by Using a Combination Network Model. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_33
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DOI: https://doi.org/10.1007/978-3-031-20099-1_33
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