In fault diagnosis, machine learning theories are gaining popularity as they proved to be an
efficient tool that not only reduces human effort but also identifies the health conditions of the …

Advanced analytics are fundamental to transform large manufacturing data into resourceful
knowledge for various purposes. In its very nature, such “industrial big data” can relay its …

Industrial systems resources are capable of producing large amount of data. These data are
often in heterogeneous formats and distributed, yet they provide means to mine the …

Deep learning networks are widely used to realize the intelligent diagnosis of gear faults.
However, the problem of the insufficient number of typical fault samples and strong noise …

Fault diagnosis of rolling bearings is very important for monitoring the health of rotating
machinery. However, in actual industrial production, owing to the constraints of conditions …

The collection of large volumes of temporal data during the production process is
streamlined in a cyber manufacturing environment. The ineluctable abnormal patterns in …

Seats are important components in automobiles, susceptible to vibration and fatigue caused
by random road spectra. To address this issue, we introduce three methods for predicting …

A multidomain variance-learnable prototypical network (MVPN) is proposed to learn
transferable knowledge from a large-scale dataset containing sufficient samples of multiple …

Accurately identifying bearing faults in aeroengines is crucial for maintaining their lifespan
and cost. However, most current models are black-box models, such as deep learning …

A great percentage of breakdowns in the rotary machines are caused by faulty gears and
bearings generating high costly downtime. The diagnosis of gears and bearings combined …