Temporal attention-integrated simulation modelling of automotive component degradation for remaining useful life rediction

Predicting the remaining useful life of automotive components is vital for safety and reliability in dynamic operating environments.Existing data-driven methods often miss the temporal dynamics and evolving patterns in sensor data, limiting prediction accuracy.Propose a novel simulation modelling framework that merges a physics-informed degradation simulator with a deep learning network augmented by multi-head temporal attention.This fusion generates realistic degradation trajectories while the attention mechanism dynamically prioritises key time-based features for precise life estimation.Testing on a public turbofan engine dataset shows model achieves a mean absolute error of 12.8 cycles and a root mean square error of 16.3 cycles, surpassing conventional long short-term memory and convolutional neural network models by 18.7% and 23.4%, respectively.The attention outputs provide interpretable views into critical degradation phases, offering a robust and insightful tool for prognostics and health management in automotive systems.