Comparison of Different Excitation Strategies for Fault Diagnosis of Belt Drives

Industrial Application Scenarios

verfasst von

Moritz Johannes Fehsenfeld, Johannes Kühn, Zygimantas Ziaukas, Hans-Georg Jacob

Abstract

Machine learning (ML) has received a lot of attention in solving fault diagnosis (FD) tasks. As a result, more and more advanced machine learning algorithms have been developed to increase accuracy. But the system’s excitation has likewise a high impact on the diagnosis performance and applicability. For this purpose, we describe different industrial application scenarios and the related set trajectory. They are divided into passive FD, where normal operation data serves as the input, and active FD, where an optimized excitation is injected. All scenarios are investigated concerning achievable accuracy and data requirement based on comprehensive measurements. We demonstrate that in active scenarios a high accuracy of 97.6 % combined with a small number of measurements are obtained by very basic algorithms like a one-nearest neighbor with Euclidean distance. In passive scenarios, where the FD task is generally harder, the demand for large datasets and more advanced ML methods increases. In this way, we illustrate how intelligent use of an optimized excitation strategy leads to feasible, reliable, and accurate fault diagnosis with a broad industrial application spectrum.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Identifikation & Regelung

Externe Organisation(en)

Lenze SE

Typ

Aufsatz in Konferenzband

Seiten

177-184

Publikationsdatum

03.08.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Artificial intelligence, Signalverarbeitung

Elektronische Version(en)

https://doi.org/10.5220/0011274100003271 (Zugang: Offen)