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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

ISBN

979-8-3503-3182-0

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE

Místo vydání

Singapur

Místo konání akce

Singapore

Datum konání akce

16. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—