Lifetime Prediction Considering Nonlinearity in Degradation Progression

In this paper, the establishment of a prognostic approach based on a nonlinear degradation model for reliability assessment focusing on health state and remaining useful life estimation is considered. A model able to describe the non-linearity of degradation to predict future damage progression for real-time application has to be defined. Real-time data are generated during operation, so incomplete data about failure and usage up to the end of life are expected. For the accurate prediction of system lifetime, estimation of future degradation from the point of assessment is required. At this point, the unavailable data are numerically calculated by integrating linearized gradients adaptively by considering nonlinearity in current degradation. The coefficients used to define future degradation gradients are identified according to different states assuming future linear degradation increments. These coefficients are determined using an optimization-based algorithm simultaneously with the calculation of consumed lifetime by extending the previously established state machine lifetime model. For performance evaluation of the approach, the effectiveness of predicting remaining useful life using tribological experiments data is investigated. The results show the potential of this approach to deal with nonlinearity in the degradation progression.