Abstract
The double tapered roller bearing is widely used in mechanical equipment, due to its complex structure, traditional safety detection is difficult to recognize early weak fault. In order to solve this problem, a deep learning method for safety detection of roller bearing is put forward. In experiment, vibration signals of bearing are firstly separated into a series of intrinsic mode functions by empirical mode decomposition, then we extracted the transient energy to construct eigenvectors. In pattern recognition, deep learning method is used to generate safety detector by unsupervised study. There are three states rolling bearings in experiments, as normal, inner fault and outer fault. The results show that the proposed method is more stable and accurately to identify bearing faults, and the classification accuracy is 98%.
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Acknowledgments
This paper was supported by the National Natural Science Foundation of China (Grant No. 11702091), and the Natural Science Foundation of Hunan Province of China (Grant No. 2018JJ3140).
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Tao, J., Zhang, S., Yang, D. (2018). The Safety Detection for Double Tapered Roller Bearing Based on Deep Learning. In: Wang, G., Chen, J., Yang, L. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2018. Lecture Notes in Computer Science(), vol 11342. Springer, Cham. https://doi.org/10.1007/978-3-030-05345-1_42
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DOI: https://doi.org/10.1007/978-3-030-05345-1_42
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