This study investigates the application of physics-informed neural networks (PINN) for
bending and free vibration analysis of three-dimensional functionally graded (TDFG) porous …

The phase design of high entropy alloys (HEAs) is an important issue since the phase
structure affects the comprehensive properties of HEAs. Accurate prediction of phase …

Blade crack detection is the key to ensuring the smooth and safe operation of centrifugal
fans. However, a single vibration signal is difficult to fully reflect the health state of the blade …

Imputation of missing data is a task that plays a vital role in a number of engineering and
science applications. Often such missing data arise in experimental observations from …

The safe navigation of unmanned surface vehicles in the marine environment requires multi-
sensor collaborative perception, and multi-sensor data fusion technology is a prerequisite …

To mitigate the impact of fault iconic feature shift and feature missing due to missing data
values on bearing fault diagnosis, this paper proposes a fault diagnosis method based on a …

Abstract Addressing uncertainty in Deep Learning (DL) is essential, as it enables the
development of models that can make reliable predictions and informed decisions in …

Vibration data is one of the most informative data to be used for fault detection. It mostly
employs in the form of frequency response function (FRF) for training deep learners …

In the recent years, machine learning techniques, notably deep learning, have emerged as
vital tools in diverse arenas ranging from image recognition to agriculture, medicine, civil …

In the working state of the aero engine, it is very important to accurately monitor the vibration
characteristics of the rotor blades to obtain their operating status, aiming to avoid serious …