期刊
ENTROPY
卷 20, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/e20120944
关键词
Naive Bayes; remaining useful life; root mean square
资金
- National Natural Science Foundation of China [71601022]
- Natural Science Foundation of Beijing [4173074]
- Key Project B Class of Beijing Natural Science Fund [KZ201710028028]
- Capacity Building for Sci-Tech Innovation-Fundamental Scientific Research Funds [025185305000-187]
- Youth Innovative Research Team of Capital Normal University
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.
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