TY - GEN
T1 - Lifetime Evaluation Method Based on Small Samples and Multi-Source Data
AU - Chen, Yazeng
AU - Fu, Guicui
AU - Leng, Hongyan
AU - Zhong, Ling
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/4
Y1 - 2019/1/4
N2 - For the spaceborne components with long lifetime and high reliability, we may get a relatively small number of lifetime samples, but the sample may have more sources. Therefore, how to effectively use these small sample multi-source data is a difficult problem. This paper proposes a comprehensive lifetime assessment method for the spaceborne components which is based on small sample multi-source data and uses Bayesian theory to fuse all data. Firstly, the small sample data of the spaceborne components is regarded as the sample information in the Bayes theory, and the other information of the lifetime data is regarded as the prior information of the Bayes theory. Secondly, according to the method of determining the weight value of the second-type maximum likelihood estimation to obtain the best prior distribution, Multi-source prior information is weighted fusion. Then, the best prior distribution is combined with small sample data to obtain the Bayes posteriori distribution. In the end the comprehensive lifetime assessment of spaceborne component is completed. Finally, the proposed method is used to estimate the lifetime data of a certain type of device.
AB - For the spaceborne components with long lifetime and high reliability, we may get a relatively small number of lifetime samples, but the sample may have more sources. Therefore, how to effectively use these small sample multi-source data is a difficult problem. This paper proposes a comprehensive lifetime assessment method for the spaceborne components which is based on small sample multi-source data and uses Bayesian theory to fuse all data. Firstly, the small sample data of the spaceborne components is regarded as the sample information in the Bayes theory, and the other information of the lifetime data is regarded as the prior information of the Bayes theory. Secondly, according to the method of determining the weight value of the second-type maximum likelihood estimation to obtain the best prior distribution, Multi-source prior information is weighted fusion. Then, the best prior distribution is combined with small sample data to obtain the Bayes posteriori distribution. In the end the comprehensive lifetime assessment of spaceborne component is completed. Finally, the proposed method is used to estimate the lifetime data of a certain type of device.
KW - Bayes method
KW - Lifetime evaluation
KW - Multi-source data
KW - Spaceborne components
UR - https://www.scopus.com/pages/publications/85061785111
U2 - 10.1109/PHM-Chongqing.2018.00118
DO - 10.1109/PHM-Chongqing.2018.00118
M3 - 会议稿件
AN - SCOPUS:85061785111
T3 - Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
SP - 659
EP - 663
BT - Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
A2 - Ding, Ping
A2 - Li, Chuan
A2 - Yang, Shuai
A2 - Ding, Ping
A2 - Sanchez, Rene-Vinicio
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
Y2 - 26 October 2018 through 28 October 2018
ER -