Abstract
Aiming at the problem of incomplete information covered by the bearing state discrimination using a single physical quantity as the data source, a characterization method based on the feature information fusion of multi-physical quantities including acceleration RMS and stiffness that are sensitive to the bearing operating state is proposed. The accelerated life test of the bearing is carried out through the bearing experiment bench, and the effect of different feature information fusion schemes to characterize the bearing state is compared, and the effectiveness of the method is verified. This paper also analyzes the different characteristics of the state evolution law between the bearing early failure caused by specific factors and the bearing reaching normal fatigue life, which provides a new method and idea for the maintenance strategy of rolling bearing such as condition-based maintenance.
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Abbreviations
- x(i):
-
Vibration signals
- n :
-
The number of vibration signal data points
- \({{\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\frown$}}\over x} }_{t\left| {t - 1} \right.}}\) :
-
State estimation of all information before t−1 at time t
- \({\hat X}\) :
-
Fused data
- W i :
-
Weighting factor, \(\sum\limits_{i = 1}^n {{W_i} = 1} \)
- X i :
-
Data to be fused
- e(t):
-
The estimation error at time t
- K t :
-
Kalman gain matrix
- I :
-
The identity matrix
- m :
-
Mass of eccentricity
- k :
-
Stiffness of bearing
- Z k+1 :
-
Observations at the time of tk+1
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Acknowledgments
This work was supported by Research Program supported by the National Key R&D Program of China (No.2018YFB2000300), the High-level Talents of Dalian City (No.2018RQ18), China.
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Ning Li is a doctor student of the School of Mechanical Engineering, Dalian University of Technology, Dalian, China. He received a bachelor’s degree from Changan University in 2014 and a master’s degree from Dalian Jiaotong University in 2018. He is mainly engaged in the research of vibration signal analysis and condition identification of mechanical transmission system.
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Li, N., Yun, X., Han, Q. et al. Characterization method of rolling bearing operation state based on feature information fusion. J Mech Sci Technol 37, 1197–1205 (2023). https://doi.org/10.1007/s12206-023-0207-1
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DOI: https://doi.org/10.1007/s12206-023-0207-1