Article: Temporal attention-integrated simulation modelling of automotive component degradation for remaining useful life rediction Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.33 pp.89 - 105 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Li Wang

Addresses: School of Automotive Engineering, Lanzhou Petrochemical University of Vocational Technology, Lanzhou, 730060, China

Abstract: 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.

Keywords: remaining useful life; RUL; temporal attention; deep learning; degradation simulation; automotive components.

DOI: 10.1504/IJICT.2026.152917

International Journal of Information and Communication Technology, 2026 Vol.27 No.33, pp.89 - 105

Received: 17 Jan 2026
Accepted: 18 Feb 2026
Published online: 14 Apr 2026 *

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

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