Leveraging Degradation Testing and Condition Monitoring for Field Reliability Analysis With Time-Varying Operating Missions

Traditionally, degradation testing and condition monitoring are used separately to investigate field reliability. Barriers are naturally formed between these two types of methods due to condition-discrepancies between lab testing and field monitoring, as well as time-varying missions among product population groups. In this paper, a joint framework for field reliability analysis is presented by integrating degradation testing data as well as mission operating information with condition monitoring observations. A coherent modeling strategy is introduced for the information integration by gradually adopting random effects, dynamic covariates, and marker processes into a baseline stochastic degradation model. In detail, random effects are introduced to cope with the inherent unit-to-unit variation. Dynamic covariates are adopted to deal with the external condition heterogeneity. Marker processes are used to account for the time-varying missions. To facilitate information integration and reliability analysis, the Bayesian method is used to implement parameter estimation and degradation analysis. The reliability assessment of products´ populations, degradation prediction, and residual life prediction of individual products are investigated. Finally, an illustrative example for field degradation analysis of oil debris in a lubrication system of a machine tool´s spindle system is presented. The effectiveness of information integration and the capability of degradation inference are demonstrated through this example.