Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an
accurate and timely diagnosis method is necessary. With the breakthrough in deep learning …

Because multi-fault vibration signals in rotating machinery are often more complicated than
single faults, human-designed fault feature sets are not yet able to respond adequately to …

Currently, convolutional neural networks (CNNs) have shown great potential in the field of
rotating machinery fault diagnosis. To maximize accuracy, the network architecture of novel …

Fault diagnosis has a great significance in preventing serious failures of rotating machinery
and avoiding huge economic losses. The performance of the existing fault diagnosis …

Recently, deep neural network (DNN) models work incredibly well, and edge computing has
achieved great success in real-world scenarios, such as fault diagnosis for large-scale …

Fault diagnosis is important for the maintenance of rotating machinery. The detection of
faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this …

When an abnormal situation occurs in rotating machinery, fault feature information may be
scattered on multiple sensors, and fault feature extraction through a single sensor is not …

The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating
machinery (RM) have critical importance for early diagnosis to prevent severe damage of …

Rotating machinery plays a key role in mechanical equipment, and the fault diagnosis of
rotating machinery is a popular research topic. To overcome the dependency on expert …

Condition monitoring of rotating machinery is important to promptly detect early faults,
identify potential problems, and prevent complete failure. Four direct classification methods …