ABSTRACT
This chapter focuses on the study of early warning of failures or defects for a general class of highly reliable product testing data, which is accumulated in a time sequence. It proposes a Bayesian on-line method to make early warning of defects for highly reliable products. The test data are usually binary but with highly sparse defects or failures occurring in a long period of testing. Two ways are utilized to pre-process the test data: aggregating the test data by a fixed-time interval or by a fixed number of test specimens. The chapter presents a detailed early warning scheme based on the Bayesian predictive approach. A Bayesian on-line algorithm is presented to make early warning of defects based on the two models for the pre-processed the data from both schemes. The proposed algorithm is applied to a set of field mobile phone test data and turns out to be quite satisfactory.
