Digital Twin for rotating machinery fault diagnosis in smart manufacturing

Jinjiang Wang, Lunkuan Ye, Robert X. Gao, Chen Li and Laibin Zhang

International Journal of Production Research, 2019, vol. 57, issue 12, 3920-3934

Abstract: With significant advancement in information technologies, Digital Twin has gained increasing attention as it offers an enabling tool to realise digitally-driven, cloud-enabled manufacturing. Given the nonlinear dynamics and uncertainty involved during the process of machinery degradation, proper design and adaptability of a Digital Twin model remain a challenge. This paper presents a Digital Twin reference model for rotating machinery fault diagnosis. The requirements for constructing the Digital Twin model are discussed, and a model updating scheme based on parameter sensitivity analysis is proposed to enhance the model adaptability. Experimental data are collected from a rotor system that emulates an unbalance fault and its progression. The data are then input to a Digital Twin model of the rotor system to investigate its ability of unbalance quantification and localisation for fault diagnosis. The results show that the constructed Digital Twin rotor model enables accurate diagnosis and adaptive degradation analysis.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2018.1552032 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:57:y:2019:i:12:p:3920-3934

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2018.1552032

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().