Deep learning methods have been widely investigated in machinery fault diagnosis owing to
their powerful feature learning capability. However, high accuracy is hard to achieve due to …

Wind power has developed rapidly over the past decade where study on wind turbine fault
diagnosis methods are of great significance. The conventional intelligent diagnosis …

The fault diagnostics of rotating components are crucial for most mechanical systems since
the rotating components faults are the main form of failures of many mechanical systems. In …

Sparse fault transient extraction is the primary step in rotating machine fault detection. In the
present paper, periodical convolutional sparse representation (PCSR) is proposed for …

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction …

Fault diagnosis of rotating machinery is difficult under the strong noisy environment.
Although singular value decomposition (SVD) can remove noise from vibration signals, the …

Fault diagnosis of rotating machinery is crucial to improve safety, enhance reliability and
reduce maintenance cost. The manual feature extraction and selection of traditional fault …

Bearing fault diagnosis has been a challenge in the monitoring activities of rotating
machinery, and it's receiving more and more attention. The conventional fault diagnosis …

Deep learning is characterized by strong self-learning and fault classification ability without
manually feature extraction stage of traditional algorithms. Deep belief network (DBN) is one …

As well-known, deep learning models have achieved great success in the field of intelligent
fault diagnosis. However, once the working condition changed, the diagnostic accuracy of …