Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples

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QIN, Luosheng ;SHEN, Xuejin ;CHEN, Xiaoyang ;GAO, Pandong .
Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 63, n.4, p. 248-254, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/reliability-assessment-of-bearings-based-on-performance-degradation-values-under-small-samples/>. Date accessed: 16 apr. 2021. 
doi:http://dx.doi.org/10.5545/sv-jme.2016.3898.
Qin, L., Shen, X., Chen, X., & Gao, P.
(2017).
Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples.
Strojniški vestnik - Journal of Mechanical Engineering, 63(4), 248-254.
doi:http://dx.doi.org/10.5545/sv-jme.2016.3898
@article{sv-jmesv-jme.2016.3898,
	author = {Luosheng  Qin and Xuejin  Shen and Xiaoyang  Chen and Pandong  Gao},
	title = {Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {63},
	number = {4},
	year = {2017},
	keywords = {bearings; distribution-based degradation; small sample; bootstrapping method; Monte Carlo method; reliability},
	abstract = {It is difficult to obtain the lifetime data of a long lifetime bearing from a test with limited time. Therefore, to apply the method of reliability assessment based on lifetime data to the high reliability and long lifetime bearings would be impractical. The performance degradation data, which contains reliability information, could be used in the reliability assessment. However, the methods based on performance degradation data are often applied in a large sample situation. In this paper, a method suitable for a small-sample situation based on a distribution-based degradation model and a bootstrapping method combined with the Monte Carlo method (DDBMC) is proposed. This method is put forward to enlarge the sample size and estimate the distribution parameters. Then, the function between distribution parameters and time can be obtained by using the least square method. In this paper, the reliability of the ball bearings under a small sample is assessed to verify the proposed method. Finally, the proposed methodology was applied to assessing the reliability of bearings and shown to be efficient in the reliability assessment of bearings under small samples.},
	issn = {0039-2480},	pages = {248-254},	doi = {10.5545/sv-jme.2016.3898},
	url = {https://www.sv-jme.eu/article/reliability-assessment-of-bearings-based-on-performance-degradation-values-under-small-samples/}
}
Qin, L.,Shen, X.,Chen, X.,Gao, P.
2017 June 63. Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 63:4
%A Qin, Luosheng 
%A Shen, Xuejin 
%A Chen, Xiaoyang 
%A Gao, Pandong 
%D 2017
%T Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples
%B 2017
%9 bearings; distribution-based degradation; small sample; bootstrapping method; Monte Carlo method; reliability
%! Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples
%K bearings; distribution-based degradation; small sample; bootstrapping method; Monte Carlo method; reliability
%X It is difficult to obtain the lifetime data of a long lifetime bearing from a test with limited time. Therefore, to apply the method of reliability assessment based on lifetime data to the high reliability and long lifetime bearings would be impractical. The performance degradation data, which contains reliability information, could be used in the reliability assessment. However, the methods based on performance degradation data are often applied in a large sample situation. In this paper, a method suitable for a small-sample situation based on a distribution-based degradation model and a bootstrapping method combined with the Monte Carlo method (DDBMC) is proposed. This method is put forward to enlarge the sample size and estimate the distribution parameters. Then, the function between distribution parameters and time can be obtained by using the least square method. In this paper, the reliability of the ball bearings under a small sample is assessed to verify the proposed method. Finally, the proposed methodology was applied to assessing the reliability of bearings and shown to be efficient in the reliability assessment of bearings under small samples.
%U https://www.sv-jme.eu/article/reliability-assessment-of-bearings-based-on-performance-degradation-values-under-small-samples/
%0 Journal Article
%R 10.5545/sv-jme.2016.3898
%& 248
%P 7
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 63
%N 4
%@ 0039-2480
%8 2018-06-27
%7 2018-06-27
Qin, Luosheng, Xuejin  Shen, Xiaoyang  Chen, & Pandong  Gao.
"Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples." Strojniški vestnik - Journal of Mechanical Engineering [Online], 63.4 (2017): 248-254. Web.  16 Apr. 2021
TY  - JOUR
AU  - Qin, Luosheng 
AU  - Shen, Xuejin 
AU  - Chen, Xiaoyang 
AU  - Gao, Pandong 
PY  - 2017
TI  - Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2016.3898
KW  - bearings; distribution-based degradation; small sample; bootstrapping method; Monte Carlo method; reliability
N2  - It is difficult to obtain the lifetime data of a long lifetime bearing from a test with limited time. Therefore, to apply the method of reliability assessment based on lifetime data to the high reliability and long lifetime bearings would be impractical. The performance degradation data, which contains reliability information, could be used in the reliability assessment. However, the methods based on performance degradation data are often applied in a large sample situation. In this paper, a method suitable for a small-sample situation based on a distribution-based degradation model and a bootstrapping method combined with the Monte Carlo method (DDBMC) is proposed. This method is put forward to enlarge the sample size and estimate the distribution parameters. Then, the function between distribution parameters and time can be obtained by using the least square method. In this paper, the reliability of the ball bearings under a small sample is assessed to verify the proposed method. Finally, the proposed methodology was applied to assessing the reliability of bearings and shown to be efficient in the reliability assessment of bearings under small samples.
UR  - https://www.sv-jme.eu/article/reliability-assessment-of-bearings-based-on-performance-degradation-values-under-small-samples/
@article{{sv-jme}{sv-jme.2016.3898},
	author = {Qin, L., Shen, X., Chen, X., Gao, P.},
	title = {Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {63},
	number = {4},
	year = {2017},
	doi = {10.5545/sv-jme.2016.3898},
	url = {https://www.sv-jme.eu/article/reliability-assessment-of-bearings-based-on-performance-degradation-values-under-small-samples/}
}
TY  - JOUR
AU  - Qin, Luosheng 
AU  - Shen, Xuejin 
AU  - Chen, Xiaoyang 
AU  - Gao, Pandong 
PY  - 2018/06/27
TI  - Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 63, No 4 (2017): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2016.3898
KW  - bearings, distribution-based degradation, small sample, bootstrapping method, Monte Carlo method, reliability
N2  - It is difficult to obtain the lifetime data of a long lifetime bearing from a test with limited time. Therefore, to apply the method of reliability assessment based on lifetime data to the high reliability and long lifetime bearings would be impractical. The performance degradation data, which contains reliability information, could be used in the reliability assessment. However, the methods based on performance degradation data are often applied in a large sample situation. In this paper, a method suitable for a small-sample situation based on a distribution-based degradation model and a bootstrapping method combined with the Monte Carlo method (DDBMC) is proposed. This method is put forward to enlarge the sample size and estimate the distribution parameters. Then, the function between distribution parameters and time can be obtained by using the least square method. In this paper, the reliability of the ball bearings under a small sample is assessed to verify the proposed method. Finally, the proposed methodology was applied to assessing the reliability of bearings and shown to be efficient in the reliability assessment of bearings under small samples.
UR  - https://www.sv-jme.eu/article/reliability-assessment-of-bearings-based-on-performance-degradation-values-under-small-samples/
Qin, Luosheng, Shen, Xuejin, Chen, Xiaoyang, AND Gao, Pandong.
"Reliability Assessment of Bearings Based on Performance Degradation Values under Small Samples" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 63 Number 4 (27 June 2018)

Authors

Affiliations

  • Shanghai University, School of Mechatronic Engineering and Automation, China 1

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 63(2017)4, 248-254

https://doi.org/10.5545/sv-jme.2016.3898

It is difficult to obtain the lifetime data of a long lifetime bearing from a test with limited time. Therefore, to apply the method of reliability assessment based on lifetime data to the high reliability and long lifetime bearings would be impractical. The performance degradation data, which contains reliability information, could be used in the reliability assessment. However, the methods based on performance degradation data are often applied in a large sample situation. In this paper, a method suitable for a small-sample situation based on a distribution-based degradation model and a bootstrapping method combined with the Monte Carlo method (DDBMC) is proposed. This method is put forward to enlarge the sample size and estimate the distribution parameters. Then, the function between distribution parameters and time can be obtained by using the least square method. In this paper, the reliability of the ball bearings under a small sample is assessed to verify the proposed method. Finally, the proposed methodology was applied to assessing the reliability of bearings and shown to be efficient in the reliability assessment of bearings under small samples.

bearings; distribution-based degradation; small sample; bootstrapping method; Monte Carlo method; reliability