The role of data fusion in predictive maintenance using digital twin

By Zheng Liu, N. Meyendorf, Nezih Mrad. 2018

324 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/21c06e665a07613122c2f8542e0e7f5152bdd640

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Zhong Fan
2. Aidan Fuller
3. Noël Crespi
4. Charles Day
5. G. Lee
6. Dusist Niyato
7. Roberto Minerva

In Safety, Risk, Reliability and Quality

1. Zhong Fan
2. Aidan Fuller
3. Charles Day

Citations in the corpus

• Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020, background): “This ability is an enabler for data fusion [49].”
• Digital Twin: Enabling Technologies, Challenges and Open Research (2020, methodology): “…digital twin is a computerized model of a physical device or system that represents all functional features and links with the working elements.” c) Liu et al. 2018 [7] “The digital twin is actually a living model of the physical asset or system, which continually adapts to operational changes…”
• Digital Twin: Origin to Future (2021, background): “In future, DT technology can be combined with other technologies for better results, e.g., mixed reality with DT technology can be used for better visualization in DT as well as for remotely supervised inspections [86].”
• A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, methodology): “This is accomplished through modeling, and data fusion [44], [45].”