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. 2023 Jun 4;23(11):5325.
doi: 10.3390/s23115325.

Dynamic Evaluation of the Degradation Process of Vibration Performance for Machine Tool Spindle Bearings

Liang Ye  1 , Wenhu Zhang  1 , Yongcun Cui  1 , Sier Deng  1   2
Affiliations

Dynamic Evaluation of the Degradation Process of Vibration Performance for Machine Tool Spindle Bearings

Liang Ye et al. Sensors (Basel). .

Abstract

Real-time condition monitoring and fault diagnosis of spindle bearings are critical to the normal operation of the matching machine tool. In this work, considering the interference of random factors, the uncertainty of the vibration performance maintaining reliability (VPMR) is introduced for machine tool spindle bearings (MTSB). The maximum entropy method and Poisson counting principle are combined to solve the variation probability, so as to accurately characterize the degradation process of the optimal vibration performance state (OVPS) for MTSB. The dynamic mean uncertainty calculated using the least-squares method by polynomial fitting, fused into the grey bootstrap maximum entropy method, is utilized to evaluate the random fluctuation state of OVPS. Then, the VPMR is calculated, which is used to dynamically evaluate the failure degree of accuracy for MTSB. The results show that the maximum relative errors between the estimated true value and the actual value of the VPMR are 6.55% and 9.91%, and appropriate remedial measures should be taken before 6773 min and 5134 min for the MTSB in Case 1 and Case 2, respectively, so as to avoid serious safety accidents that are caused by the failure of OVPS.

Keywords: degradation probability; machine tool spindle bearings; optimal performance state; uncertainty; vibration performance maintaining reliability.

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Conflict of interest statement

The authors declare that there is no conflict of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
Performance degradation and reliability evaluation process of rolling bearings.
Figure 2
Figure 2
Flow diagram of proposed method.
Figure 3
Figure 3
Vibration signals of MTSB.
Figure 4
Figure 4
Probability density function of data samples of intrinsic series.
Figure 5
Figure 5
Variation probability curve.
Figure 6
Figure 6
Variation probability curve fitting using least-square method.
Figure 7
Figure 7
Sample data generated using grey bootstrap method.
Figure 8
Figure 8
Maximum entropy probability density function of grey bootstrap samples.
Figure 9
Figure 9
Estimated results of degradation probability of OVPS of MTSB.
Figure 10
Figure 10
Dynamic evaluation results of PMR.
Figure 11
Figure 11
Error bars of PMR.
Figure 12
Figure 12
Dynamic evaluation results of PMRR.
Figure 13
Figure 13
Vibration signals of bearing performance (Case 2).
Figure 14
Figure 14
Probability density function of data samples of intrinsic series (Case 2).
Figure 15
Figure 15
Variation probability curve (Case 2).
Figure 16
Figure 16
Variation probability curve fitting using least-square method (Case 2).
Figure 17
Figure 17
Sample data using grey bootstrap method (Case 2).
Figure 18
Figure 18
Maximum entropy probability density function of grey bootstrap samples (Case 2).
Figure 19
Figure 19
Estimated results of degradation probability of OVPS of MTSB (Case 2).
Figure 20
Figure 20
Dynamic evaluation results of PMR (Case 2).
Figure 21
Figure 21
Error bars of PMR (Case 2).
Figure 22
Figure 22
Dynamic evaluation results of PMRR (Case 2).
See this image and copyright information in PMC

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