Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

Wojciech Pietrowski; Konrad Górny

doi:10.3390/en17020476

Scientific Information System of the Poznań University of Technology

PL EN

Main page / Publications / Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

Submit a comment

Article

Download file Download BibTeX

Title

Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

Authors

Wojciech Pietrowski (WARiE) ^{[ 1 ][ 2.2 ][ P ]}
Konrad Górny (WARiE) ^{[ 1 ][ 2.2 ][ P ]}

^{[ 1 ]} Instytut Elektrotechniki i Elektroniki Przemysłowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | ^{[ P ]} employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technologies

Year of publication

2024

Published in

Energies

Journal year: 2024 | Journal volume: vol. 17 | Journal number: iss. 2

Article type

scientific article

Publication language

english

Keywords

EN

induction machine
finite-element method
analysis of a current waveform
deep neural network
noninvasive diagnostics
multiphase interturn short circuits

Abstract

EN The objective of the investigation was to increase the effectiveness of damage detection in the stator of the squirrel-cage induction machine. The analysis aimed to enhance the operational trustworthiness of the squirrel-cage induction machine by employing nonintrusive diagnostic methods based on a current signal and modern artificial intelligence methods. The authors of the study introduced a diagnostic technique for identifying multiphase interturn short circuits of stator winding. These short circuits are one of the most common faults in induction machines. The proposed method focusses on deriving a diagnostic signal from the phase-current waveforms of the machine. The noninvasive nature of the diagnostic technique presented is attributed to the application of the field model of electromagnetic phenomena to determine the diagnostic signal. For this purpose, a field model of a squirrel-cage machine was developed. The waveforms of phase currents obtained from the field model were used as input into an elaborated machine failure neural classifier. A deep neural network was used to develop a neural classifier. The effectiveness of the developed classifier has been experimentally verified, and the obtained results have been presented, concluded, and discussed. The scientific novelty presented in the article is the presentation of research results on the use of a neural classifier to detect damage in all phases of the stator winding at an early stage of its appearance. The features of this type of damage are very difficult to observe in signal waveforms such as a phase current or torque.

Date of online publication

18.01.2024

Pages (from - to)

476-1 - 476-23

DOI

10.3390/en17020476

URL

https://www.mdpi.com/1996-1073/17/2/476

Comments

Article number: 476

License type

CC BY (attribution alone)