Abstract
This paper presents a method to predict the Remaining Useful Life (RUL) of bearings based on theories of Mixture of Gaussians Bayesian Belief Network (MoG-BBN) and Support Vector Data Description (SVDD). In this method, the feature vectors, which are used to train the corresponding MoG-BBN and SVDD model, are extracted from raw sensor data by using wavelet packet decomposition (WPD). Genetic algorithm is employed to determine the initial value of the variables in MoG-BBN training algorithm so that the stability of MoG-BBN can be enhanced. The two models are combined to acquire a good generalization ability. We demonstrate the effectiveness of the proposed method by using actual bearing datasets from the NASA prognostic data repository.
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References
Jammu, N.S., Kankar, P.K.: A review on prognosis of rolling element bearings. Int. J. Eng. Sci. Technol. 3(10), 7497–7503 (2011)
Lee, J., Qiu, H., Yu, G., Lin, J.: Rexnord Technical Services, IMS, University of Cincinnati. Bearing Data Set, NASA Ames Prognostics Data Repository. NASA Ames Research Center, Moffett Field, CA (2007). http://ti.arc.nasa.gov/project/prognostic-data-repository
Gebraeel, N.Z., Lawley, M.A.: A neural network degradation model for computing and updating residual life distributions. IEEE Trans. Autom. Sci. Eng. 5(1), 154–163 (2008)
Tian, Z., Wong, L., Safaei, N.: A neural network approach for remaining useful life prediction utilizing both failure and suspension histories. Mech. Syst. Signal Process. 24(5), 1542–1555 (2010)
Huang, R., Xi, L., Li, X., et al.: Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mech. Syst. Signal Process. 21(1), 193–207 (2007)
Tobon-Mejia, D.A., Medjaher, K., Zerhouni, N., et al.: Hidden Markov models for failure diagnostic and prognostic. In: Prognostics and System Health Management Conference (PHM-Shenzhen), 2011, pp. 1–8. IEEE (2011)
Tobon-Mejia, D.A., Medjaher, K., Zerhouni, N., et al.: A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. IEEE Trans. Reliab. 61(2), 491–503 (2012)
Shen, Z., He, Z., Chen, X., et al.: A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time. Sensors 12(8), 10109–10135 (2012)
Wang, H., Chen, J.: Performance degradation assessment of rolling bearing based on bispectrum and support vector data description. J. Vibr. Control 20(13), 2032–2041 (2014)
Sloukia, F., El Aroussi, M., Medromi, H., et al.: Bearings prognostic using mixture of gaussians hidden markov model and support vector machine. In: 2013 ACS International Conference on Computer Systems and Applications (AICCSA), pp. 1–4. IEEE (2013)
Zhang, X., Kang, J., Jin, T.: Degradation modeling and maintenance decisions based on Bayesian belief networks. IEEE Trans. Reliab. 63(2), 620–633 (2014)
Wald, R., Khoshgoftaar, T.M., Sloan, J.C.: Using feature selection to determine optimal depth for wavelet packet decomposition of vibration signals for ocean system reliability. In: 2011 IEEE 13th International Symposium on High-Assurance Systems Engineering (HASE), pp. 236–243. IEEE (2011)
Zhang, T., Zhang, H., Wang, Z.: Float encoding genetic algorithm and its application. J. Harbin Inst. Technol. 32(4), 59–61 (2000)
Tax, D.M.J., Duin, R.P.W.: Support vector data description[J]. Mach. Learn. 54(1), 45–66 (2004)
Acknowledgement
This work was partially supported by Chinese National Hi-Tech. R&D (863) Program under grant 2015AA042102.
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Wu, Q., Feng, Y., Huang, B. (2017). RUL Prediction of Bearings Based on Mixture of Gaussians Bayesian Belief Network and Support Vector Data Description. In: Zhang, L., Ren, L., Kordon, F. (eds) Challenges and Opportunity with Big Data. Monterey Workshop 2016. Lecture Notes in Computer Science(), vol 10228. Springer, Cham. https://doi.org/10.1007/978-3-319-61994-1_14
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DOI: https://doi.org/10.1007/978-3-319-61994-1_14
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