Abstract
For predicting the field failure rate, the manufacturer usually performs laboratory quality or stress tests before products being in the market. When the product is highly reliable and its use cycle is relatively short, lab tests related to early-stage reliability are sufficiently informative on failure rate prediction in the warranty period. Taking the smartphone as an example, drop and tumble tests are performed for the product strength under which the stress level that a specimen can withstand is treated as a reliability index. This thesis aims to incorporate these lab reliability indices to improve the warranty failures prediction. But, due to high reliability of modern products, most of testing units are passed in the lab stress test leading to very few failures or even no failures, which raises challenges for estimating the reliability index precisely. To overcome this issue, this thesis suggests a hierarchical model structure to integrate all available historical lab data from other homogeneous products to further improve the estimation precision for the reliability index when sparse lab failures are encountered in practice. Another contribution of this thesis is to perform on-line predictions for the warranty failure rates using a Bayesian framework based on a joint modeling of lab data and field data. The performance and the advantages of the proposed methodology are demonstrated by a case study with simulated data.