An innovative physics-informed machine learning framework for RUL prediction and uncertainty quantification in EVDS is proposed, which effectively integrates physical information with deep learning algorithms, yielding a more concentrated probability density distribution of RUL predictions with higher accuracy, and enhanced generalization performance.
Addressing the issues of traditional data-driven methods lacking interpretability and physics-informed neural network (PINN) being susceptible to noise due to statistical feature inputs, this paper proposes a feature fusion-based physics-informed neural network method for predicting the remaining useful life (RUL) of b...
This study presents a physics-informed decoupled machine learning framework integrated with a multi-objective Pareto Human–Machine Interface (HMI) advisory system that dynamically balances range extension against passenger thermal discomfort to deliver actionable driver recommendations.
M. Mądziel, Tiziana Campisi· Energies· 0 citations
It was shown that the proposed PINN outperforms traditional neural networks in terms of accuracy (mean absolute errors), and the use of an attention mechanism emphasizes the role played by recent driving behavior in improving the predictive performance.
K. R. Jeevakamal, Rayappa David Amar Raj, Archana Pallakonda et al.· Neural computing & applicati...· 0 citations
Rolling bearings are critical components of many machines, like high‐speed trains, wind turbines, aerospace systems, and various industrial applications, whose safety and reliability highly rely on their states. Accurately predicting components' remaining useful life is essential to avoid uninterrupted operation and re...
A. N. Sanjrani, Sadiq Ali Shah, N. Q. Soomro et al.· Engineering Reports· 0 citations
Accurately predicting electrical grid stability is essential to ensuring the reliability, resilience, and sustainable operation of modern smart energy systems. The nonlinear interactions among the grid operating parameters make stability prediction challenging, particularly when conventional machine learning models...
N. Subramanian, Albert Alexander Stonier· Frontiers in Artificial Inte...· 0 citations
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