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 fashioned between these two sorts of methods because of condition-discrepancies between lab testing and field monitoring, also time-varying missions among product population teams. During this paper, a joint framework for field reliability analysis is presented by integrating degradation testing knowledge with mission operating info with condition monitoring observations. A coherent modeling strategy is introduced for the data integration by gradually adopting random effects, dynamic covariates, and marker processes into a baseline stochastic degradation model. Well, random effects are introduced to address the inherent unit-to-unit variation. Dynamic covariates are adopted to accommodate the external condition heterogeneity. Marker processes are used to account for the time-varying missions. To facilitate data integration and reliability analysis, the Bayesian methodology is used to implement parameter estimation and degradation analysis. The reliability assessment of products' populations, degradation prediction, and residual life prediction of individual product are investigated. Finally, an illustrative example for field degradation analysis of oil debris in an exceedingly lubrication system of a machine tool's spindle system is presented. The effectiveness of information integration and the aptitude of degradation inference are demonstrated through this example.

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