Impact of the Convolutional Neural Network Structure and Training Parameters on the Effectiveness of the Diagnostic Systems of Modern AC Motor Drives

Maciej Skowron, Czeslaw T. Kowalski and Teresa Orlowska-Kowalska ()
Additional contact information
Maciej Skowron: Department of Electrical Machines and Drives, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
Czeslaw T. Kowalski: Department of Electrical Machines and Drives, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
Teresa Orlowska-Kowalska: Department of Electrical Machines and Drives, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland

Energies, 2022, vol. 15, issue 19, 1-22

Abstract: Currently, AC motors are a key element of industrial and commercial drive systems. During normal operation, the machines may become damaged, which may pose a threat to the users. Therefore, it is important to develop a fault detection method that allows for the detection of a fault at an early stage. Among the currently used diagnostic systems, applications based on deep neural structures are dynamically developed. Despite many examples of applications of deep learning methods, there are no formal rules for selecting the network structure and parameters of the training process. Such methods would make it possible to shorten the implementation process of deep networks in diagnostic systems of AC machines. The article presents a detailed analysis of the influence of deep convolutional network hyperparameters and training procedures on the precision of the interturn short-circuits detection system. The studies take into account the direct analysis of phase currents through the convolutional network for induction motors and permanent magnet synchronous motors. The research results presented in the article are an extension of the authors’ previous research.

Keywords: convolutional neural network; hyperparameters; fault detection; induction motor drive; permanent magnet synchronous motor; diagnostic system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/19/7008/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/19/7008/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:19:p:7008-:d:923844

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().