Multi-Source Domain Adaptation for Fault Diagnosis of Belt Drives

verfasst von

Moritz Fehsenfeld, Johannes Kühn, Karl-Philipp Kortmann

Abstract

The implementation effort of data-driven fault diagnosis systems greatly exceeds its economic benefits in many industrial cases. Consequently, highly adapted, individual solutions instead of widespread distribution in the market are currently the result. The biggest problem is the availability of large amounts of labeled data, in particular fault data. In this work, we propose a multi-source domain adaptation procedure that integrates synthetic fault data generation into cross-domain classifier training to overcome this issue. The approach does not require fault data in the target domain which is highly relevant in practice. It is examined using the rarely studied example of diagnosing faulty pretensioning of belt drives. Datasets for multiple domains are collected by attaching different loads to the machine. An extensive experimental study on single and multiple source domains demonstrates the effectiveness of the proposed approach. The generation of fault data outperforms the benchmark methods, especially for multi-source scenarios. Overall, the cross-domain fault diagnosis of belt drives yields promising results to enable a broad range of industrial applications.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Identifikation & Regelung

Externe Organisation(en)

Lenze SE

Typ

Artikel

Journal

IFAC-PapersOnLine

Band

56

Seiten

11305-11310

Anzahl der Seiten

6

ISSN

2405-8963

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik

Elektronische Version(en)

https://doi.org/10.1016/j.ifacol.2023.10.411 (Zugang: Geschlossen)