Implementation of ANN for PMSM interturn short-circuit detection in the embedded system

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F23%3APU149633" target="_blank" >RIV/00216305:26620/23:PU149633 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10312642" target="_blank" >https://ieeexplore.ieee.org/document/10312642</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IECON51785.2023.10312642" target="_blank" >10.1109/IECON51785.2023.10312642</a>

Result language
angličtina
Original language name
Implementation of ANN for PMSM interturn short-circuit detection in the embedded system
Original language description
The problem of condition monitoring and fault detection in powertrain systems becomes more critical with the increasing use of fail-operational systems. These systems are essential in the automotive industry, robotics, and other industrial applications. One of the critical features of such a system is recognizing the fault and suppressing its influence. The paper describes a feed-forward artificial neural network-based diagnostic of interturn short-circuit faults in a dual three-phase permanent magnet synchronous motor. The paper focuses on using MLPN, and CNN for interturn short-circuit detection and, more importantly, their real implementation into the automotive AURIX TC397 microcontroller. The paper presents the achieved neural network inference times as well as data preprocessing computation time. The behavior of the ANNs is tested on an experimental configurable multiphase PMSM drive with the possibility to emulate interturn short-circuit fault using prepared winding taps. The paper includes the essential aspects that should be respected during ANN design and implementation into the microcontroller.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society
ISBN
979-8-3503-3182-0
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1-6
Publisher name
IEEE
Place of publication
Singapur
Event location
Singapore
Event date
Oct 16, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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