The application of deep learning-based rolling bearing fault diagnosis methods in high
reliability scenarios is limited due to low transparency. In addition, the scaling up of the deep …

Due to adverse working conditions of rotating machinery in actual engineering, bearing fault
data are more difficult to acquire compared to normal data. That said, the real collected …

Asian soybean rust (ASR) is one of the major diseases that causes serious yield loss
worldwide, even up to 80%. Early and accurate detection of ASR is critical to reduce …

The burgeoning field of the Industrial Internet of Things (IIoT) necessitates advanced fault
diagnosis methods capable of navigating the dual challenges of high predictive accuracy …

Data-driven fault diagnosis methods for rotating machinery have developed rapidly with the
help of deep learning methods. However, traditional intelligent fault diagnosis methods still …

In recent years, deep learning has been applied to intelligent fault diagnosis and has
achieved great success. However, the fault diagnosis method of deep learning assumes that …

In the diagnosis of rotating machinery faults, traditional methods often fail to consider the
correlation and dependency among various grid features of different signal sources in multi …

Convolutional neural networks are widely used for feature extraction in signal recognition. A
critical issue in convolutional neural networks is the loss of information which increases with …

Bearing plays a significant role in the transmission of traction forces and safe operation of
train. Affected by the actual operating conditions of the train, it is of great significance to …

The main bearing is a key component to maintaining the stable and safe operation of the
gas generator power end, so fault diagnosis for the main bearing has special significance …