Work

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

Year: 2024

Type: conference-paper

Abstract: The challenges of fault detection and condition monitoring in powertrain systems have become increasingly prominent, particularly with the widespread adoption of fail-operational 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.

Source: Proceedings of the Annual Conference of the IEEE Industrial Electronics Society

DOI: 10.1109/iecon55916.2024.10905074

Authors: Matus Kozovsky, Ludek Buchta, Petr Blaha

Institutions: Central European Institute of Technology, Brno University of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: Ministry of Education