Publication detail

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

KOZOVSKÝ, M. BUCHTA, L. BLAHA, P.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

Neural network, fault detection, diagnostic, PMSM, motor

Authors

KOZOVSKÝ, M.; BUCHTA, L.; BLAHA, P.

Released

16. 10. 2023

Publisher

IEEE

Location

Singapur

ISBN

979-8-3503-3182-0

Book

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

Pages from

1

Pages to

6

Pages count

6

URL

https://ieeexplore.ieee.org/document/10312642

Full text in the Digital Library

http://hdl.handle.net/11012/245226

BibTex

@inproceedings{BUT185461,
  author="Matúš {Kozovský} and Luděk {Buchta} and Petr {Blaha}",
  title="Implementation of ANN for PMSM interturn short-circuit detection in the embedded system",
  booktitle="IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society",
  year="2023",
  pages="6",
  publisher="IEEE",
  address="Singapur",
  doi="10.1109/IECON51785.2023.10312642",
  isbn="979-8-3503-3182-0",
  url="https://ieeexplore.ieee.org/document/10312642"
}