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 …

This paper presents a convolutional neural network (CNN) based approach for fault
diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by …

The rolling bearings play a vital role in mechanical production and transportation. However,
when it appears abnormal, the fault characteristics are weak and different to be extracted in …

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 …

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

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 …

This paper presents a data-driven intelligent fault diagnosis approach for rotating machinery
(RM) based on a novel continuous wavelet transform-local binary convolutional neural …

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 …

Rotating machinery plays a critical role in many significant fields. However, the
unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of …

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