Abstract
This paper presents a method to predict the remaining useful life of bearings based on theories of Mixture of Gaussians Bayesian Belief Network (MoG-BBN) and Support Vector Data Description (SVDD). Our method extracts feature vectors from raw sensor data using wavelet packet decomposition (WPD). The features are then used to train the corresponding MoG-BBN and SVDD model. Genetic algorithm is employed to determine the initial value of training algorithm and enhance the stability of our model. The two models are combined to acquire a good generalization ability. The effectiveness of the proposed method is verified by actual bearing datasets from the NASA prognostic data repository.
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Acknowledgement
This work was supported by the National Hig-Tech. R&D (863) Program (No. 2015AA042102) in China.
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Wu, Q., Feng, Y., Huang, B. (2016). RUL Prediction of Bearings Based on Mixture of Gaussians Bayesian Belief Network and Support Vector Data Description. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-10-2666-9_13
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DOI: https://doi.org/10.1007/978-981-10-2666-9_13
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