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 …

The past decade has witnessed the adoption of artificial intelligence (AI) in various
applications. It is of no exception in the area of prognostics and health management (PHM) …

Bearing is one of the most crucial parts in induction motor (IM) as a result there is a constant
call for effective diagnosis of bearing faults for reliable operation. Infrared thermography …

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing
attention due to the availability of massive condition monitoring data. However, most existing …

Fault detection prior to their occurrence or complete shut-down in induction motor is
essential for the industries. The fault detection based on condition monitoring techniques …

Traditional intelligent fault diagnosis techniques based on artificially selected features fail to
make the most of the raw data information, and are short of the capabilities of feature self …

Prognostics and health management (PHM) is a framework that offers comprehensive yet
individualized solutions for managing system health. In recent years, PHM has emerged as …

Complete fault sample is essential to activate artificial intelligent (AI) models. A novel fault
detection scheme is proposed to build a bridge between AI and real-world running …

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 …

The main challenge of fault diagnosis lies in finding good fault features. A deep learning
network has the ability to automatically learn good characteristics from input data in an …