PMSM fault detection using unsupervised learning methods based on conditional convolution autoencoder

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F24%3APU154792" target="_blank" >RIV/00216305:26620/24:PU154792 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10905074" target="_blank" >https://ieeexplore.ieee.org/document/10905074</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IECON55916.2024.10905074" target="_blank" >10.1109/IECON55916.2024.10905074</a>

Result language

angličtina

Original language name

PMSM fault detection using unsupervised learning methods based on conditional convolution autoencoder

Original language description

The challenges of fault detection and condition monitoring in powertrain systems have become increasingly prominent, particularly with the widespread adoption of failoperational systems. These systems are pivotal in diverse sectors, including the robotics, automotive industry, and various industrial applications. A critical attribute of such systems lies in their capability to identify non-standard behaviour of the system. This study describes a inovative conditional convolutional autoencoder-based fault detection algorithm for the permanent magnet synchronous motor. The study compares a train process of conditional convolutional autoencoder with a classical convolutional autoencoder. The presented autoencoder structure was designed to be implementable into the target microcontroller AURIX TC397 while providing sufficient recognition capabilities of the interturn short-circuit. Autoencoders are trained on data obtained during healthy motor operation and subsequently used to detect interturn short-circuit faults on the experimental dual three-phase permanent magnet synchronous motor with the possibility of emulating an interturn short-circuit fault. The paper provides insights into the achieved autoencoder inference times and the sensitivity in detecting the fault.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20205 - Automation and control systems

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

2024

Confidentiality

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

Article name in the collection

IECON 2024- 50th Annual Conference of the IEEE Industrial Electronics Society

ISBN

978-1-6654-6454-3

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

Chicago, IL, USA

Event location

Chicago

Event date

Nov 3, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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