On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

The emergence of the Internet of Things and the interconnection of systems and machines
enables the idea of Industry 4.0, a new industrial paradigm with a strong focus on interaction …

Recent years have witnessed prominent advances in predictive maintenance (PdM) for
complex industrial systems. However, the existing PdM literature predominately separates …

This paper proposes a novel hybrid method aiming at the fault prognosis of bearings. A
nonlinear health indicator (HI) is first constructed using Complete Ensemble Empirical Mode …

In this study, a deep learning-based probabilistic remaining useful life (RUL) prediction
model is proposed to improve the strong prior limitations of traditional probabilistic RUL …

This paper deals with the problem of dynamically integrating tactical production planning
and predictive maintenance in the context of a rolling horizon approach. At the production …

This study proposed a performance-interpretable deep learning model for rolling bearing
fault diagnosis that integrates an intelligent fusion of sound and vibration signals and self …

First prediction time (FPT) detection is a significant task when conducting remaining useful
life (RUL) prediction for mechanical equipment. Nevertheless, many existing works conducts …

Estimating latent degradation states of mechanical systems from observation data provide
the basis for their prognostic and health management (PHM). Recently, deep learning …

Lightning-related failures are of great concerns for the reliable performance of overhead
contact lines (OCLs) of high-speed railway. Predicting lightning-related failure probability is …