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 …

Condition monitoring plays a vital role in ensuring the reliability and optimal performance of
various engineering systems. Traditional methods for condition monitoring rely on physics …

Various deep learning methodologies have recently been developed for machine condition
monitoring recently, and they have achieved impressive success in bearing fault …

Intelligent fault diagnosis of rolling bearings using deep learning-based methods has made
unprecedented progress. However, there is still little research on weight initialization and the …

Aiming at life-cycle condition monitoring of high-strength bolt connections, a physics-guided
deep learning framework integrating supervised and unsupervised learning was developed …

The efficient diagnosis of bearing faults requires the extraction of informative features. This
paper presents a novel approach that combines Weighted Principal Component Analysis …

Varying components and operating conditions in industrial machines lead to different
distribution characteristics and fault states of monitoring data for critical rotating machinery …

The monitoring process for complex infrastructure requires collecting various data sources
with varying time scales, resolutions, and levels of abstraction. These data sources include …

Predictive maintenance for power electronic converters has emerged as a critical area of
research and development. With the rapid advancements in deep-learning techniques, new …

Predictive maintenance is considered a proactive approach that capitalizes on advanced
sensing technologies and data analytics to anticipate potential equipment malfunctions …