Digital twin-driven intelligent assessment of gear surface degradation

verfasst von

Ke Feng, J. C. Ji, Yongchao Zhang, Qing Ni, Zheng Liu, Michael Beer

Abstract

Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

University of British Columbia
University of New South Wales (UNSW)
University of Technology Sydney
The University of Liverpool
Tongji University
Universität Nordostchinas (NEU)

Typ

Artikel

Journal

Mechanical Systems and Signal Processing

Band

186

ISSN

0888-3270

Publikationsdatum

01.03.2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik, Signalverarbeitung, Tief- und Ingenieurbau, Luft- und Raumfahrttechnik, Maschinenbau, Angewandte Informatik

Elektronische Version(en)

https://doi.org/10.1016/j.ymssp.2022.109896 (Zugang: Geschlossen)