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| Environmental Data Conversion and Fusion Method under the Weibull Distribution Scenario |
| Received:March 28, 2025 Revised:May 17, 2025 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2025.05.007 |
| KeyWord:environmental data conversion Weibull distribution life evaluation optimal linear unbiased estimation Gehan-Wilcoxon test average rank method |
| Author | Institution |
| LI Hao |
Aerospace Science & Industry Corp Defense Technology R&T Center, Beijing , China |
| XU Ruyuan |
Aerospace Science & Industry Corp Defense Technology R&T Center, Beijing , China |
| ZHU Yao |
Aerospace Science & Industry Corp Defense Technology R&T Center, Beijing , China |
| ZHANG Shengpeng |
Aerospace Science & Industry Corp Defense Technology R&T Center, Beijing , China |
| RONG Shuanglong |
Aerospace Science & Industry Corp Defense Technology R&T Center, Beijing , China |
| HUANG Shuo |
Aerospace Science & Industry Corp Defense Technology R&T Center, Beijing , China |
| ZHU Wenxi |
Aerospace Science & Industry Corp Defense Technology R&T Center, Beijing , China |
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| Abstract: |
| The work aims to achieve the comprehensive evaluation of product life in a Weibull distribution scenario through conversion and fusion of different environmental data. Based on the Nelson assumption, a general formula for the conversion of different environmental test data under a Weibull distribution scenario was constructed. The optimal linear unbiased estimation method was used to evaluate the distribution parameters under different environments. The applicability of the optimal linear unbiased estimation method was expanded by the Γ function. Ultimately, the comprehensive evaluation of the converted and fused environmental data was performed with the mean rank method and the expanded optimal linear unbiased estimation method. Finally, with the time-censored test data of a product under different environments as an example, the Gehan-Wilcoxon test method was used to quantitatively compare the data conversion effect. The results of the case data showed that according to the traditional method, the Gehan-Wilcoxon test statistic |Z| between the converted data obtained with only the ratio of the Weibull distribution scale parameters as the conversion coefficient and the original data of the target environment was 0.880 3. In contrast, the Gehan-Wilcoxon test statistic |Z| between the converted data obtained with the method proposed in this work and the original data of the target environment was 0.344 5, indicating that the converted data obtained by the latter was more similar to the original data of the target environment, demonstrating that the conversion effect of the method proposed in this work was better. Under small sample scenarios, there will be certain differences in the estimation results of the shape parameters of different environmental test data. Using the general form of the data conversion relationship can reduce the impact of small sample scenarios on the data conversion effect to a certain extent. |
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