Missing data interpolation and multi‐sensors integration and its application in accelerated degradation data

Abstract

AbstractMultiple sensors are commonly used for accelerated degradation monitoring. Since different sensors may be sensitive at different stages of the accelerated degradation process and each sensor dataset may contain only partial information of the unit degradation, then integration approaches of the accelerated degradation data from multiple sensors can effectively improve degradation modeling and life prediction accuracy. We present a non‐parametric approach that assigns weights to each sensor based on the dynamic clustering of the sensors' observations. Missing data are common in degradation data acquisition, especially when multiple sensors are used. We provide two approaches for data interpolation: the nonlinear Brownian bridge and the inverse Gaussian bridge when the underlying degradation paths follow the Brownian motion process and inverse Gaussian process, respectively. The stochastic bridges capture the nonlinearity and uncertainties of the degradation processes. The data integration model and stochastic bridge models are validated with real accelerated fatigue crack growth data monitored with multiple NDT sensors. The proposed models provide an accurate accelerated degradation path and reliability prediction.