The growing complexity of machinery and the increasing demand for operational efficiency
and safety have driven the development of advanced fault diagnosis techniques. Among …

Intelligent fault diagnosis methods based on deep learning have attracted significant
attention in recent years. However, it still faces many challenges, including complex and …

The occurrence of bearing faults is often accompanied by noise signals, and noise sensors
have the characteristics of non-contact and flexible arrangement; hence, this paper …

Mechanical information is a medium for perceptual interaction and health monitoring of
organisms or intelligent mechanical equipment, including force, vibration, sound, and flow …

In bearing fault diagnosis, the faulty data are generally limited due to the high cost of fault
signal collection. Considering the excessive parameters in the traditional convolutional …

Under nonlinear and nonstationary dynamic conditions, the fault diagnosis methods based
on multidimensional dimensionless indicators (MDIs) often cannot provide effective and …

Convolutional neural networks (CNNs) have been widely applied to motor fault diagnosis.
However, to obtain high recognition accuracy, massive training data are typically required …

In recent years, transformer has become an effective tool for fault diagnosis, but it has been
shown that a sufficient amount of labeled data is usually required to train a transformer …

Recently, deep learning has been widely used for intelligent fault diagnosis of rolling
bearings due to its no-mankind feature extraction capability. The majority of intelligent …

Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial
for ensuring normal machinery operation. However, the nonlinear and non-stationary …