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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References
Qian, W., Li, S., Wang, J., et al.: A novel supervised sparse feature extraction method and its application on rotating machine fault diagnosis. Neurocomputing 320, 129–140 (2018)
Wang, Z.-Y., Chen, L., Zhou, B., et al.: Fault diagnosis for rotary machinery with selective ensemble neural networks. Mech. Syst. Signal Process. 113, 112–130 (2018)
Liu, Z., Pan, D., Zuo, M., et al.: A review on fault diagnosis for rail vehicles. J. Mech. Eng. 52(14), 124–141 (2016)
Liao, Y., Liu, Y., Yang, S., et al.: Numerical simulation and experimental study of a railway vehicle roller bearing with outer ring fault. J. Vib. Meas. Diagn. 34(3), 539–594 (2014)
El-Thalji, I., Jantunen, E.: A summary of fault modelling and predictive health monitoring of rolling element bearings. Mech. Syst. Signal Process. 60, 252–272 (2015)
Yao, Y., Zhang, X.: Bearing fault diagnosis of a permanent magnet synchronous motor via a fast and online order analysis method in an embedded system. Mech. Syst. Signal Process. 113, 36–49 (2018)
He, G., Xin, Z., Zuo, C., et al.: Fault diagnosis method for rolling bearing of metro vehicle based on EMD and SVM. Railw. Comput. Appl. 24(8), 1–5 (2015)
Liu, J., Zhao, Z., Zhang, G., et al.: Research on fault diagnosis method for Bogie bearings of metro vehicle. J. China Railw. Soc. 37(1), 30–36 (2015)
Glowacza, A., Glowacza, W., Glowaczb, Z., et al.: Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals. Measurement 113, 1–9 (2018)
Wang, L., Liu, Z., Miao, Q., et al.: Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis. Mech. Syst. Signal Process. 103, 60–75 (2018)
Wen, C., Lv, F., Bao, Z., Liu, M.: A review of data driven-based incipient fault diagnosis. Acta Autom. Sin. 42(9), 1285–1299 (2016)
Jiao, L., Yang, S., Liu, F., et al.: Seventy years beyond neural networks: retrospect and prospect. Chin. J. Comput. 39(8), 1697–1716 (2016)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Hinton, G.E., Salakhutdinov, R.: Reduction the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61(9), 85–117 (2015)
Tamilselvan, P., Wang, Y., Wang, P.: Deep belief network based state classification for structural health diagnosis. Reliab. Eng. Syst. Saf. 115(3), 124–135 (2013)
Tran, V.T., Althobiani, F., Ball, A.: Anapproach to fault diagnosis of reciprocating compressor valves using Teager–Kaeser energy operator and deep belief networks. Expert Syst. Appl. 41(9), 4113–4122 (2014)
Shao, H., Jiang, H., Zhang, X., et al.: Rolling bearing fault diagnosis using an optimization deep belief network. Meas. Sci. Technol. 26(11), 1–17 (2015)
Shao, H., Jiang, H., Zhang, H., et al.: Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech. Syst. Signal Process. 100, 743–765 (2018)
Lei, Y., Jia, F., Lin, J., et al.: An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans. Ind. Electron. 63(5), 3137–3147 (2016)
Gan, M., Wang, C.: Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech. Syst. Signal Process. 72, 92–104 (2016)
Li, W., Shan, W., Zeng, X.: Bearing fault identification based on deep belief network. J. Vib. Eng. 29(2), 340–347 (2016)
Chen, Z., Li, W.: Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Trans. Instrum. Meas. 66(7), 1693–1702 (2017)
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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