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Open AccessArticle

Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions

by Nannan Zhang 1,2,3,4, Lifeng Wu 1,2,3,4,*, Zhonghua Wang 1,2,3,4 and Yong Guan 1,2,3,4
1
College of Information Engineering, Capital Normal University, Beijing 100048, China
2
Beijing Key Laboratory of Electronic System Reliability Technology, Capital Normal University, Beijing 100048, China
3
Beijing Key Laboratory of Light Industrial Robot and Safety Verification, Capital Normal University, Beijing 100048, China
4
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(12), 944; https://doi.org/10.3390/e20120944
Received: 12 November 2018 / Revised: 3 December 2018 / Accepted: 5 December 2018 / Published: 8 December 2018
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
Bearing plays an important role in mechanical equipment, and its remaining useful life (RUL) prediction is an important research topic of mechanical equipment. To accurately predict the RUL of bearing, this paper proposes a data-driven RUL prediction method. First, the statistical method is used to extract the features of the signal, and the root mean square (RMS) is regarded as the main performance degradation index. Second, the correlation coefficient is used to select the statistical characteristics that have high correlation with the RMS. Then, In order to avoid the fluctuation of the statistical feature, the improved Weibull distributions (WD) algorithm is used to fit the fluctuation feature of bearing at different recession stages, which is used as input of Naive Bayes (NB) training stage. During the testing stage, the true fluctuation feature of the bearings are used as the input of NB. After the NB testing, five classes are obtained: health states and four states for bearing degradation. Finally, the exponential smoothing algorithm is used to smooth the five classes, and to predict the RUL of bearing. The experimental results show that the proposed method is effective for RUL prediction of bearing. View Full-Text
Keywords: Naive Bayes; remaining useful life; root mean square Naive Bayes; remaining useful life; root mean square
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Zhang, N.; Wu, L.; Wang, Z.; Guan, Y. Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions. Entropy 2018, 20, 944.

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