Condition Monitoring using Domain-Adversarial Networks with Convolutional Kernel Features

verfasst von

Cesar Caceres-Castellanos, Karl-Philipp Kortmann, Moritz Johannes Fehsenfeld

Abstract

The data-based condition monitoring and diagnosis of a mechatronic system can be a challenge due to the amount of labeled data traditional methods require. Moreover, transferring a trained classification model from its source domain to another mechatronic system is a difficult task due to even minor differences between sensors, dimensions, or environmental conditions. Additionally, labeled data may not be available or difficult to obtain in this new target domain. In this paper, a novel approach to time series based domain adaptation is proposed by modifying a Domain-Adversarial Neural Network. Therefore, a MiniRocket transform is combined with an artificial neural network as a composed feature extractor. This model aims to extract domain invariant features from multivariate time series data that can be used for cross-domain condition monitoring of mechatronic systems. The model is tested for belt tension monitoring using data from two belt drives considering two types of excitation. Experimental results for wideband excitation show that the proposed model estimates the tension of the belt with high accuracy in the target domain (unsupervised). For the jerk-limited excitation, accuracy is improved for the target domain in a semi-supervised setting.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Identifikation & Regelung

Typ

Paper

Seiten

7746-7752

Anzahl der Seiten

7

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Artificial intelligence

Elektronische Version(en)

https://doi.org/10.1016/j.ifacol.2023.10.1180 (Zugang: Offen)