Time-dependent reliability prediction using transfer learning

One of the key challenges in reliability estimation is the acquisition of failure information especially under real-life scenarios or computationally expensive long-run simulations. Due to the lack of failure information, it becomes a challenging task to estimate time-dependent reliability of a structure or component. In this paper, a preliminary study of time-dependent reliability prediction using transfer learning is given by utilizing only current time performance function information. Transfer learning, specifically domain adaptation, is used in this work in order to find a representation in latent space where the statistical properties of nonstationary stochastic processes such as variance are preserved and their distribution parameters at different times are made close to each other. Correlated random samples of the stochastic processes are generated in present and future time intervals in order to find common latent space using domain adaptation via transfer component analysis (TCA). Due to the excellent computational efficiency, a Kriging surrogate model is built in the present interval only using an adaptive sampling strategy. The reliability in the future is predicted using the same surrogate model without retraining it using future performance function information. Monte Carlo simulation (MCS) is used to validate the results of the proposed method. The results show that the proposed method can predict the failure probability of time-dependent problems efficiently with significant accuracy.