Detecting faults in high-speed and high-power diesel engines under complex variable
operating conditions is highly challenging. Online vibration monitoring systems have been …

In practical engineering, the working conditions of gearbox are complex and variable. In
varying working conditions, the performance of intelligent fault diagnosis model is degraded …

Gearbox failures can lead to substantial damage, significant financial losses due to
maintenance downtimes, and, in some instances, fatalities. This study introduces an …

The fault diagnosis of rolling bearings is very important in industrial applications, which can
avoid accidents and reduce operation and maintenance costs. Although the position of the …

Bearing fault diagnosis is vital for mechanical maintenance and fault prediction. It ensures
equipment safety, extends lifespan, reduces maintenance costs, and improves production …

Many current fault diagnosis methods tend to ignore the temporal correlation in signals,
leading to a loss of critical fault information. Additionally, traditional diagnostic models often …

This study examines the creation and assessment of a deep learning-based method for the
early identification of surface impurities and damage, such as dust, snow, bird droppings …

Reliability is a central aspect of machine learning applications, especially in fault diagnosis
systems, where only an accurate and reliable diagnosis system is economically justifiable …

The machine learning-based intelligent fault diagnosis method has the merits of fast
response speed and automation, but requires many fault samples which are difficult to …