As an emerging non-contact method of flow monitoring, radar technology compensates for
the shortcomings of traditional methods in terms of the efficiency, timeliness, and difficulty in …

Motors are the higher energy-conversion devices that consume around 40% of the global
electrical generated energy. Induction motors are the most popular motor type due to their …

The application of deep learning-based rolling bearing fault diagnosis methods in high
reliability scenarios is limited due to low transparency. In addition, the scaling up of the deep …

Deep learning (DL) has demonstrated splendid performance in fault diagnosis with sufficient
samples and ideal operating environments. However, in practice, it is hard to acquire …

Induction machines (IMs) are utilized in different industrial sectors such as manufacturing,
transportation, transmission, and energy due to their ruggedness, low cost, and high …

This paper proposes the rotor speed estimation of a squirrel cage induction motor fed by a
sinusoidal power supply or an inverter based on the use of a Hall effect sensor installed …

This study proposes the utilization of an optical fiber vibration sensor for detecting the
superposition of extremely close frequencies in vibration signals. Integration of deep neural …

Induction machines (IMs) are essential components of many industrial installations and,
therefore, their faults must be detected early. Fault detection using current spectrum analysis …

In this paper, a new technique based on the manipulation of stator current signature for
induction machines fault diagnosis is introduced. The goal of the proposed method is to …

Blade and winding failure often occur in the marine current turbine (MCT), which is usually
subject to marine biofouling effects. Realizing the accurate fault diagnosis of MCT is of great …