The Mean Time to Failure (MTTF) is a critical metric for assessing the reliability of non-repairable systems, and it plays a significant role in incident management. However, accurately estimating MTTF can be challenging due to the expensive physics-based simulation models. To address this challenge, this paper proposes an adaptive surrogate modeling method that approximates the failure modes in simulation model with a computationally efficient model to predict the MTTF during the design stage. Firstly, the proposed method initially trains Gaussian process (GP) surrogate models for the failure modes. Then, the composite expected feasibility function is proposed to identify the new information, such as input variables, time instances, and component index, to refine the surrogate models. In the end, the MTTF can be calculated by taking the expected value of the system's time to the first failure with the available GP models. The proposed method has the capability of forecasting MTTF for series systems, parallel systems, and mixed systems. To showcase its efficacy, we provide a mathematic and two physicsbased simulation examples, which demonstrate the adaptive surrogate modeling method can accurately predict the MTTF of the system in physics-based simulation model.

• Published 1 paper as the 3rd author on Reliability Engineering & System Safety