Digital-Triplet: a new three entities digital-twin paradigm for equipment fault diagnosis

Huang Zhang, Zili Wang (), Shuyou Zhang, Lemiao Qiu, Yang Wang, Feifan Xiang, Zhiwei Pan, Linhao Zhu and Jianrong Tan
Additional contact information
Huang Zhang: Zhejiang University
Zili Wang: Zhejiang University
Shuyou Zhang: Zhejiang University
Lemiao Qiu: Zhejiang University
Yang Wang: Zhejiang University
Feifan Xiang: Zhejiang University
Zhiwei Pan: Zhejiang University
Linhao Zhu: Canny Elevator Co., Ltd.
Jianrong Tan: Zhejiang University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 7, No 21, 4895-4914

Abstract: Abstract Current equipment fault diagnosis faces challenges due to the difficulties in arranging sensors to collect effective data and obtaining diverse fault data for studying fault mechanisms. The lack of data results in disconnection between data from different spaces, posing a challenge to forming a closed loop of data and hindering the development of digital twin (DT) driven fault diagnosis (FD). To address these issues, a new DT paradigm Digital-Triplet is proposed. This paradigm comprises three entities: a physical entity, a semi-physical entity, and a virtual entity. A semi-physical entity is created by implementing the "six-D" process on the physical entity. A new six dimensional structure is formed through the addition of the semi-physical entity. The new structure streamlines the construction of fault datasets, enhances sensor data acquisition, and tightly links different data spaces, thereby promoting the application of DT in equipment FD. Subsequently, the elevator is selected as a case study to illustrate the Digital-Triplet framework in detail. The results demonstrate that the Digital-Triplet framework can effectively expand the fault dataset and improve data collection efficiency through optimized sensor placement, thereby promoting fault diagnosis.

Keywords: Digital twin (DT); Fault diagnosis (FD); Sensors; Data connectivity (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02471-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02471-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02471-7

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().