PMSM fault detection using unsupervised learning methods based on conditional convolution autoencoder
Loading...
Date
2024-11-03
Authors
Kozovský, Matúš
Buchta, Luděk
Blaha, Petr
0000-0002-1547-1003Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Altmetrics
Abstract
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.
Description
Citation
IECON 2024- 50th Annual Conference of the IEEE Industrial Electronics Society. 2024, p. 1-6.
https://ieeexplore.ieee.org/document/10905074
https://ieeexplore.ieee.org/document/10905074
Document type
Peer-reviewed
Document version
Accepted version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
(C) IEEE