Although the diagnosis methods of rotating machinery based on convolutional neural
network (CNN) have achieved great success, they generally assume the number of normal …

Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery.
Most methods used in fault diagnosis of rotating machinery extract a few feature values from …

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 …

Fault diagnosis of rotating machinery is essential for maintaining system performance and
ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and …

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 …

The traditional fault diagnosis methods of rotating machinery based on deep learning have
made some achievements. However, the fault samples are generally difficult to collect …

Intelligent machinery fault diagnosis system has been receiving increasing attention recently
due to the potential large benefits of maintenance cost reduction, enhanced operation safety …

Fault diagnosis is very important to ensure the efficiency and reliability of rotating machinery.
Traditional fault diagnosis methods often require manual feature design and extraction …

The fast and efficient fault diagnosis is the key to guarantee uninterrupted working of
facilities, which is more frugal and trustworthy than scheduled upkeep. At present, data …

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 …