Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …

This paper provides a comprehensive literature review on Predictive Maintenance (PdM)
with emphasis on system architectures, purposes and approaches. In industry, any outages …

The fault diagnosis (FD) of rolling bearing (RB) has a great significance in safe operation of
engineering equipment. Many intelligent diagnosis methods have been successfully …

Bearing fault diagnosis is an important part of rotating machinery maintenance. Existing
diagnosis methods based on single-modal signals not only have unsatisfactory accuracy …

Because of the complex operating environment of high-end industrial machinery, rolling
bearing is generally operated at fluctuating working conditions such as variable speeds or …

Detection accuracy of bearing faults is crucial in saving economic loss for industrial
applications. Deep learning is capable of producing high accuracy for bearing fault …

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …

With the development of intelligent manufacturing and automation, the precision and
complexity of mechanical equipment are increasing, which leads to a higher requirement for …

Deep transfer learning algorithm is regarded as a promising method to address the issue of
rolling bearing fault diagnosis with limited labeled data. Stacked autoencoder (SAE) has …

Rolling bearings are essential components in a wide range of equipment, such as
aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete …