A novel machine learning-based imputation strategy for missing data in step-stress accelerated degradation test
Yaqiu Li,
Qijie Zhou,
Ye Fan,
Guangze Pan,
Zongbei Dai,
Baimao Lei
Affiliations
Yaqiu Li
China Electronic Product Reliability and Environmental Testing Research Institute, No. 76, West Zhucun Avenue, Guangzhou, China; Key Laboratory of Active Medical Devices Quality & Reliability Management and Assessment, No. 76, West Zhucun Avenue, Guangzhou, China
Qijie Zhou
China Electronic Product Reliability and Environmental Testing Research Institute, No. 76, West Zhucun Avenue, Guangzhou, China; Key Laboratory of Active Medical Devices Quality & Reliability Management and Assessment, No. 76, West Zhucun Avenue, Guangzhou, China
Ye Fan
Beijing Institute of Structure and Environment Engineer, No.1, South Dahongmen Avenue, Beijing, China
Guangze Pan
China Electronic Product Reliability and Environmental Testing Research Institute, No. 76, West Zhucun Avenue, Guangzhou, China; Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology, No. 76, West Zhucun Avenue, Guangzhou, China; Corresponding author. China Electronic Product Reliability and Environmental Testing Research Institute, No. 76, West Zhucun Avenue, Guangzhou, China.
Zongbei Dai
China Electronic Product Reliability and Environmental Testing Research Institute, No. 76, West Zhucun Avenue, Guangzhou, China
Baimao Lei
China Electronic Product Reliability and Environmental Testing Research Institute, No. 76, West Zhucun Avenue, Guangzhou, China
The presence of missing data is a significant data quality issue that negatively impacts the accuracy and reliability of data analysis. This issue is especially relevant in the context of accelerated tests, particularly for step-stress accelerated degradation tests. While missing data can occur due to objective factors or human error, high missing rate is an inevitable pattern of missing data that will occur during the conversion process of accelerated test data. This type of missing data manifests as a degradation dataset with unequal measuring intervals. Therefore, developing a more appropriate imputation method for accelerated test data is essential. In this study, we propose a novel hybrid imputation method that combines the LSSVM and RBF models to address missing data problems. A comparison is conducted between the proposed model and various traditional and machine learning imputation methods using simulation data, to justify the advantages of the proposed model over the existing methods. Finally, the proposed model is implemented on real degradation datasets of the super-luminescent diode (SLD) to validate its performance and effectiveness in dealing with missing data in step-stress accelerated degradation test. Additionally, due to the generalizability of the proposed method, it is expected to be applicable in other scenarios with high missing data rates.