Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

With the increase of the scale and complexity of mechanical equipment, traditional intelligent
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …

Three-phase induction motors are used in the industry commonly for example woodworking
machines, blowers, pumps, conveyors, elevators, compressors, mining industry, automotive …

Digital twin (DT) is a key technology for realizing the interconnection and intelligent
operation of the physical world and the world of information and provides a new paradigm …

Condition monitoring schemes are essential for increasing the reliability and ensuring the
equipment efficiency in industrial processes. The feature extraction and dimensionality …

In recent times, digital twins (DT) is becoming an emerging and key technology for smart
industrial control systems and Industrial Internet of things (IIoT) applications. The DT …

Bearing faults are a major source of failure in an induction motor, and early detection of fault
becomes necessary because of its industrial application. A range of analytical methods has …

Fault detection and diagnosis in modern control systems have been of constant interest in
recent publications. Its progress is a consequence of the requirements imposed by the …

Fault diagnosis is a top-priority task for the health management of manufacturing processes.
Deep learning-based methods are widely used to secure high fault diagnosis accuracy …

Production processes in industrial facilities are essentially a dynamic activity, but traditional
time-domain analysis (TDA) and frequency-domain analysis (FDA) assume stationary …