Maintaining electrical machines in good working order and increasing their life expectancy
is one of the main challenges. Precocious and accurate detection of faults is crucial to this …

In this paper, the idea of using a convolutional neural network (CNN) for the detection and
classification of induction motor stator winding faults is presented. The diagnosis inference …

Reliable fault diagnosis and condition monitoring are essential for permanent magnet
synchronous motor (PMSM) drive systems with high-reliability requirements. PMSMs can be …

The significant advantages of permanent magnet synchronous motors, such as very good
dynamic properties, high efficiency and power density, have led to their frequent use in …

The article proposes the use of PCA transformation carried out with the use of a neural
network as a method for compress data obtained from geodetic measurements. In this study …

Permanent magnet synchronous motors (PMSMs) have become one of the most important
components of modern drive systems. Therefore, fault diagnosis and condition monitoring of …

Permanent magnet synchronous motors (PMSM) have become one of the most substantial
components of modern industrial drives. These motors, like all the others, can unfortunately …

Permanent magnet synchronous motors (PMSMs) are increasingly used in industrial drive
applications. However, these motors can also undergo various failures, causing production …

This paper presents the application of shallow neural networks (SNNs): multi-layer
perceptron (MLP) and self-organizing Kohonen maps (SOMs) to the early detection and …

Fault diagnosis of induction motors through the analysis of the stator current is increasingly
being used in maintenance systems, because it is non-invasive and has low requirements of …