Accelerated life tests for log-normal series system with dependent masked data under Type-I progressive hybrid censoring

ABSTRACT

This article considers the constant stress accelerated life test for series system products, where independent log-normal distributed lifetimes are assumed for the components. Based on Type-I progressive hybrid censored and masked data, the expectation-maximization algorithm is applied to obtain the estimation for the unknown parameters, and the parametric bootstrap method is used for the standard deviation estimation. In addition, Bayesian approach combining latent variable with Gibbs sampling is developed. Further, the reliability functions of the system and components are estimated at use stress level. The proposed method is illustrated through a numerical example under different masking probabilities and censoring schemes.

KEYWORDS:

• Accelerated life tests
• Dependent masked data
• Expectation-maximization algorithm
• Gibbs sampling
• Log-normal distribution
• Masking probabilities

MATHEMATICAL SUBJECT CLASSIFICATION:

• 62N01
• 62N05
• 62F15

Acknowledgments

This work is supported by the National Natural Science Foundation of China (71171164, 71401134, 71571144), the Natural Science Basic Research Program of Shaanxi Province (2015JM1003), and the Science and Technology Program of Guizhou Province ([2016]1073).