Spatial-temporal data analysis of digital twin [version 1; peer review: 1 approved, 2 approved with reservations]
Background: Digital Twin (DT) has proven to be one of the most promising technologies for routine monitoring and management of complex systems with uncertainties. Methods: Our work, which is mainly concerned with heterogeneous spatial-temporal data, focuses on exploring data utilization methodology...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
F1000 Research Ltd
2022-04-01
|
| Series: | Digital Twin |
| Subjects: | |
| Online Access: | https://digitaltwin1.org/articles/2-7/v1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Background: Digital Twin (DT) has proven to be one of the most promising technologies for routine monitoring and management of complex systems with uncertainties. Methods: Our work, which is mainly concerned with heterogeneous spatial-temporal data, focuses on exploring data utilization methodology in DT. The goal of this research is to summarize the best practices that make the spatial-temporal data analytically tractable in a systematic and quantifiable manner. Some methods are found to handle those data via jointly spatial-temporal analysis in a high-dimensional space effectively. We provide a concise yet comprehensive tutorial on spatial-temporal analysis considering data, theories, algorithms, indicators, and applications. The advantages of our spatial-temporal analysis are discussed, including model-free mode, solid theoretical foundation, and robustness against ubiquitous uncertainty and partial data error. Finally, we take the condition-based maintenance of a real digital substation in China as an example to verify our proposed spatial-temporal analysis mode. Results: Our proposed spatial-temporal data analysis mode successfully turned raw chromatographic data, which are valueless in low-dimensional space, into an informative high-dimensional indicator. The designed high-dimensional indicator could capture the ’insulation’ correlation among the sampling data over a long time span. Hence it is robust against external noise, and may support decision-making. This analysis is also adaptive to other daily spatial-temporal data in the same form. Conclusions: This exploration and summary of spatial-temporal data analysis may benefit the fields of both engineering and data science. |
|---|---|
| ISSN: | 2752-5783 |