CNN-based fault diagnosis approaches have achieved promising results in improving the
safety and reliability of rotating machinery. Most of the existing CNN models are developed …

Recent advances in convolutional neural networks (CNN) have boosted the research on
reliability monitoring of rotating machinery. In actual industry production, the mechanical …

Extracting robust fault sensitive features of vibration signals remains a challenge for rotating
machinery fault diagnosis under variable operating conditions. Most existing fault diagnosis …

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 …

Deep learning methods have been widely used in the field of fault diagnosis of rotating
machinery. As one of the deep learning methods, a multiscale convolutional neural network …

Fault diagnosis is of great importance for rotating machinery maintenance. Deep learning is
an intelligent diagnosis technology that attracts more attention at present. The ability to learn …

Deep learning methods have been widely used in the field of intelligent fault diagnosis due
to their powerful feature learning and classification capabilities. However, it is easy to overfit …

Accurate and efficient fault diagnosis in rotating machinery has long been important and
challenging, as it strongly affects the system reliability and safety of industrial applications. In …

The rapid development of deep learning raises a new research area for condition monitoring
and fault diagnosis of mechanical equipment recently. However, the amount of labeled fault …

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in
the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of …