Updating digital twins: Methodology for data accuracy quality control using machine learning techniques

Rodríguez, Fabio; Chicaiza, William D.; Sánchez, Alfonso; Escaño, Juan Manuel

doi:10.1016/j.compind.2023.103958

Cited by 11 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed modelling approach integrates principal component analysis (PCA) with ANFIS, following the methodology presented in [29,33]. PCA is applied to normalised data sets to reduce their input space, resulting in the loading matrix s C, which contains the coefficients of the principal components of each variable.…”

Section: Datamentioning

confidence: 99%

Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

Ranade,

Gómez,

de Juan

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency.

show abstract

Section: Datamentioning

confidence: 99%

Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

Ranade,

Gómez,

de Juan

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Control theory addresses how input affects output to achieve stable, fast, and accurate operation of a physical system 66 . Figure 12 illustrates corresponding technologies like PID control 67 , adaptive control 68 , and model predictive control 69 , which design appropriate controllers and algorithms by the features and goals of physical entities. Thus, data information from DT models can be transformed into effective regulations for abnormal situation.…”

Section: Dt Modeling Tech Tool and Dt System For Phmmentioning

confidence: 99%

Digital twin-driven prognostics and health management for industrial assets

Xiao,

Zhong,

Bao

et al. 2024

Sci Rep

View full text Add to dashboard Cite

As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in prognostics and health management (PHM). Faults can lead to degradation or malfunction of industrial assets. Accordingly, DT-driven PHM studies are conducted to improve reliability and reduce maintenance costs of industrial assets. However, there is a lack of systematic research to analyze and summarize current DT-driven PHM applications and methodologies for industrial assets. Therefore, this paper first analyzes the application of DT in PHM from the application field, aspect, and hierarchy at application layer. The paper next deepens into the core and mechanism of DT in PHM at theory layer. Then enabling technologies and tools for DT modeling and DT system are investigated and summarized at implementation layer. Finally, observations and future research suggestions are presented.

show abstract

Detecting broken receiver tubes in CSP plants using intelligent sampling and dual loss

Pérez-Cutiño,

Valverde,

Díaz-Báñez

2023

Appl Intell

View full text Add to dashboard Cite

Updating digital twins: Methodology for data accuracy quality control using machine learning techniques

Cited by 11 publications

References 23 publications

Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

Digital twin-driven prognostics and health management for industrial assets

Detecting broken receiver tubes in CSP plants using intelligent sampling and dual loss

Contact Info

Product

Resources

About