Simultaneous in-situ calibration for physical and virtual sensors towards digital twin-enabled building operations

Koo, Jabeom; Yoon, Sungmin

doi:10.1016/j.aei.2023.102239

Cited by 4 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 3 presents a selection of studies in fundamental research of digital twins. The study on sensor calibration within building systems [ 28 ] demonstrates the ongoing effort to synchronize physical and virtual sensor data, which is a crucial step for accurate digital twin simulations. Research into sensor reliability [ 29 ] tackles the challenge of predictive maintenance by using redundant digital sensors to foresee potential sensor failures.…”

Section: Resultsmentioning

confidence: 99%

Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis

Li,

Zhou,

et al. 2024

Sensors

View full text Add to dashboard Cite

Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration.

show abstract

Section: Resultsmentioning

confidence: 99%

Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis

Li,

Zhou,

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The method showed effective performance with a small number of datasets. To address the problem of virtual sensor errors having a negative effect on the in situ calibration accuracy of the corresponding physical sensors, Koo et al [ 33 ] proposed and adopted a simultaneous in situ calibration method. Li et al [ 34 ] proposed a general-regression-improved Bayesian inference calibration method with a calibration accuracy of over 97%.…”

Section: Introductionmentioning

confidence: 99%

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments

Li,

Wang,

Liu

et al. 2024

Sensors

View full text Add to dashboard Cite

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

show abstract

In-situ observation and calibration for structure safety diagnosis through finite element analysis and mixed reality

Zhao,

Li,

Sun

et al. 2024

Advanced Engineering Informatics

View full text Add to dashboard Cite

Simultaneous in-situ calibration for physical and virtual sensors towards digital twin-enabled building operations

Cited by 4 publications

References 47 publications

Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis

Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments

In-situ observation and calibration for structure safety diagnosis through finite element analysis and mixed reality

Contact Info

Product

Resources

About