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RUL Prediction of Bearings Based on Mixture of Gaussians Bayesian Belief Network and Support Vector Data Description

  • Qianhui Wu
  • Yu Feng
  • Biqing HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10228)

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.

Keywords

Wavelet packet decomposition Mixture of gaussians bayesian belief network Genetic algorithm Support vector data description Remaining useful life 

Notes

Acknowledgement

This work was partially supported by Chinese National Hi-Tech. R&D (863) Program under grant 2015AA042102.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of AutomationTsinghua UniversityBeijingChina

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