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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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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