In this paper, we develop an integrated prognostics method considering a time-varying operating condition, which integrates physical gear models and sensor data. By taking advantage of stress analysis in finite element modeling (FEM), the degradation process governed by Paris' law can adjust itself immediately to respond to the changes of the operating condition. The capability to directly relate the load to the damage propagation is a key advantage of the proposed integrated prognostics approach over the existing data-driven methods for dealing with time-varying operating conditions. In the proposed method, uncertainties in material parameters are considered as sources responsible for randomness in the predicted failure life. The joint distribution of material parameters is updated as sensor data become available. The updated distribution better characterizes the material parameters, and reduces the uncertainty in life prediction for the specific individual unit under condition monitoring. The update process is realized via Bayesian inference. To reduce the computational effort, a polynomial chaos expansion (PCE) collocation method is applied in computing the likelihood function in the Bayesian inference and the predicted failure time distribution. Examples based on crack propagation in a spur gear tooth are given to demonstrate the effectiveness of the proposed method. In addition, the example also shows that the proposed approach is effective even when the current loading profile is different from the loading profile under which historical data were collected.Index Terms-Bayesian update, integrated prognostics, polynomial chaos expansion, time-varying operating condition, uncertainty quantification.
ACRONYMS AND ABBREVIATIONS: FEM finite element modeling PCE polynomial chaos expansion RUL remaining useful life PHM proportional hazard model FE finite element SIF stress intensity factor Manuscript
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