Predictive maintenance is a well studied collection of techniques that aims to prolong the life
of a mechanical system by using artificial intelligence and machine learning to predict the …

This article proposes a new framework using physics-informed neural networks (PINNs) to
simulate complex structural systems that consist of single and double beams based on Euler …

Multimodality is a universal characteristic of multi-source monitoring data for rotating
machinery. The correlation fusion of multimodal information is a general law to strengthen …

Artificial intelligence (AI) has shown great potential in the maintenance stage of industrial
manufacturing. However, the existing data-driven methods often lack integration with …

In mechanical anomaly detection, algorithms with higher accuracy, such as those based on
artificial neural networks, are frequently constructed as black boxes, resulting in opaque …

Fault prognosis is an important area of research that aims to predict and diagnose faults in
complex systems. The sudden failure of industrial components can have adverse …

Modeling technology is both the core and the difficulty of digital twin. In response to this
challenge, a digital twin framework based on dynamic model is proposed and applied to …

Data-driven approaches are promising to address the modeling issues of modern power
electronics-based power systems, due to the black-box feature. Frequency-domain analysis …

This paper proposes a hybrid approach incorporating a physical model into generative
adversarial networks for unsupervised fault detection of underwater thrusters. Specifically, a …

Remaining useful life (RUL) prediction has great significance in reducing operating costs
and enhancing the maintainability and safety of rolling bearings. Recently, significant …