RUL Estimation

Remaining useful life estimation uses sensor histories and degradation signals to predict the time or cycles left before an asset reaches failure or a defined end-of-life threshold. Common approaches include recurrent and convolutional networks, Transformers, survival models, and physics-informed methods that constrain predictions with degradation mechanisms; some also estimate uncertainty or combine models. Evaluation often uses turbofan benchmarks such as NASA C-MAPSS and battery-aging datasets, with growing attention to partial observations, changing operating conditions, and data from multiple organizations. A central challenge is making predictions reliable beyond benchmark settings, while balancing accuracy with calibrated uncertainty, robust evaluation, and useful maintenance timing.