Rolling bearing condition monitoring method based on multi-feature information fusion

Yanfei ZHANG; Yunhao LI; Lingfei KONG; Wenchao LI; Yanjing Yi

doi:10.51393/j.jamst.2022020

Volume 3 Issue 1

Oct. 2022

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Article Navigation > Journal of Advanced Manufacturing Science and Technology > 2023 > 3(1): 2022020

Yanfei ZHANG, Yunhao LI, Lingfei KONG, Wenchao LI, Yanjing Yi. Rolling bearing condition monitoring method based on multi-feature information fusion[J]. Journal of Advanced Manufacturing Science and Technology , 2023, 3(1): 2022020. doi: 10.51393/j.jamst.2022020

Citation:

Yanfei ZHANG, Yunhao LI, Lingfei KONG, Wenchao LI, Yanjing Yi. Rolling bearing condition monitoring method based on multi-feature information fusion[J]. Journal of Advanced Manufacturing Science and Technology , 2023, 3(1): 2022020. doi: 10.51393/j.jamst.2022020

Citation:

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Rolling bearing condition monitoring method based on multi-feature information fusion

doi: 10.51393/j.jamst.2022020

a School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology;Xi'an 710048, China;
b Luoyang Bearing Science&Technology Co., Ltd., Luoyang 471039, China;
c Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China

Funds:

This study was supported by the Shaanxi Provincial Key R&D Program of China (2022GY-211)

Received Date: 2022-05-21
Rev Recd Date: 2022-07-24

Available Online: 2022-09-13

Publish Date: 2022-09-13

Abstract

Abstract

In machining operations, the misalignment of the bearing assembly or imbalanced load often leads to deflection and failure of the tool spindle. The use of single feature information does not accurately monitor the complex working conditions. Considering this, this paper proposes a rolling bearing running condition monitoring method which is based on multiple feature information. Firstly, a multi-dimensional feature matrix is obtained by extracting the features of a single type of raw data in the time domain, frequency domain, and time-frequency domain, and then the dimensionality of the matrix is reduced by principal component analysis (PCA). An entropy weight improved the D-S(EWID-S) evidence theory is proposed. By updating the initial evidence source, and applying the Euclidean distance of the spatial centroid, the fusion results were evaluated. Finally, a test rig for eccentric bearing load operation is developed to obtain the vibration signals at two distinct locations and to confirm the proposed method. The test results show that the condition monitoring method based on the PCA and EWID-S evidence theory can effectively identify the bearing operating at different degrees of deflection. At the same time, by comparing with other improved D-S evidence theory methods, it is verified that this method has more advantages in information fusion and bearing condition monitoring.
- Condition monitoring,
- PCA dimensionality reduction,
- D-S evidence theory,
- Information fusion,
- Euclidean distance

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References(23)

References

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