Within smart manufacturing, data driven techniques are commonly adopted for condition
monitoring and fault diagnosis of rotating machinery. Classical approaches use supervised …

Abstract Industry 4.0 denotes smart manufacturing, where rotating machines predominantly
serve as the fundamental components in production sectors. The primary duty of …

In the realm of industrial production, condition monitoring plays a pivotal role in ensuring the
reliability and longevity of rotating machinery. Since most of the production facilities rely …

Rotating machinery are an important part of industrial equipment. Their components are
subjected to harsh operating environments, and hence experience significant wear and tear …

Electric machines and motors have been the subject of enormous development. New
concepts in design and control allow expanding their applications in different fields. The vast …

Introduction Intelligent diagnostics is the most important issue in the predictive maintenance
of industrial rotating machinery. Analytical diagnostics tools have been proved effective …

The internet of things (IOT) enabled presence of abundant sensors on smart machineries
and the recent advance in deep learning is accelerating the development of predictive …

Rotating machinery is prone to unexpected faults and failures as they usually undergo
challenging operating conditions, such as high and varying speed and load. If the fault is not …

In many industrial applications, only healthy condition monitoring data is available for
rotating machines that are newly put into operation or have not met the onset of degradation …

Vibration signals are closely linked with health conditions of rotating machines and widely
used in fault diagnosis. Unfortunately, traditional vibration signal-based fault diagnosis …