Browsing by Author "Fernández del amo blanco, Iñigo"

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    Data: A Design Framework for Adaptive Digital Twins
    (Cranfield University, 2023-09-04 09:26) ahmet Erkoyuncu, John; Fernández del amo blanco, Iñigo; Ariansyah, Dedy; Bulka, Dominik; Vrabič, Rok; Roy, Rajkumar
    This paper develops a new DT design framework that uses ontologies to enable co-evolution with the CES by capturing data in terms of variety, velocity, and volume across the asset life-cycle. The framework has been tested successfully on a helicopter gearbox demonstrator and a mobile robotic system across their life cycles, illustrating DT adaptiveness without the data architecture needing to be modified. The data presented in this portal is related to the data that was generated in the validation process.
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    ItemOpen Access
    Datasets: Hybrid recommendations and dynamic authoring for AR knowledge capture and re-use in maintenance diagnosis applications
    (Cranfield University, 2020-06-02 16:15) Fernández del amo blanco, Iñigo; ahmet Erkoyuncu, John; Farsi, Maryam
    This repository includes datasets on experimental cases of study and analysis regarding the research called " Hybrid recommendations and dynamic authoring for AR knowledge capture and re-use in maintenance diagnosis applications". DOI: Abstract: “In Industry 4.0, integrated data management is an important challenge due to heterogeneity and lack of structure of numerous existing data sources. A relevant research gap involves human knowledge integration, especially in maintenance operations. Augmented Reality (AR) can bridge this gap but requires improved augmented content to enable effective and efficient knowledge capture. This paper proposes dynamic authoring and hybrid recommender methods for accurate AR-based reporting. These methods aim to provide maintainers with augmented data input formats and recommended datasets for enhancing efficiency and effectiveness of their reporting tasks. This research validated the proposed contributions through experiments and surveys in two failure diagnosis reporting scenarios. Experimental results indicated that the proposed reporting solution can reduce reporting errors by 50% and reporting time by 20% compared to alternative recommender and AR tools. Besides, survey results suggested that testers perceived the proposed reporting solution as more effective and satisfactory for reporting tasks than alternative tools. Thus, proving that the proposed methods can improve effectiveness and efficiency of diagnosis reporting applications. Finally, this paper proposes future works towards a framework for automatic adaptive authoring in AR knowledge transfer and capture applications for human knowledge integration in the context of Industry 4.0.”
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    ItemOpen Access
    Datasets: Ontology-based diagnosis reporting and monitoring to improve fault finding in Industry 4.0
    (Cranfield University, 2020-08-14 09:41) Fernández del amo blanco, Iñigo; ahmet Erkoyuncu, John; Farsi, Maryam; Bulka, Dominik; Wilding, Stephen
    This repository includes datasets on experimental cases of study and analysis regarding the research called "Ontology-based diagnosis reporting and monitoring to reduce no-fault-found scenarios in Industry 4.0".DOI:Abstract: "Industry 4.0 is bringing a new era of digitalisation for complex equipment. It especially benefits equipment’s monitoring and diagnostics with real-time analysis of heterogenous data sources. Management of such sources is an important research challenge. A relevant research gap involves integration of experts’ diagnosis knowledge. Experts have valuable knowledge on failure conditions that can support monitoring systems and their limitations in no-fault-found scenarios. But their knowledge is normally transferred as reports, which include unstructured data difficult to re-use. Thus, this paper proposes ontology-based diagnosis reporting and monitoring methods to capture and re-use expert knowledge for improving diagnosis efficiency. It aims to capture expert knowledge in a structured format and re-use it in monitoring systems to provide failure recommendations in no-fault-found conditions. This research conducted several methods for validating the proposed methods. Laboratory experiments present time and errors reduction rates of 20% and 12% compared to common data-driven monitoring approaches for diagnosis tasks in no-fault-found scenarios. Subject-matter experts’ surveys evidence the usability of the proposed methods to work in real-life conditions. Thus, this paper’s proposal can be considered as a method to bridge the gap for integrated data management in the context of Industry 4.0."
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    Datasets: Programmable content and a pattern-matching algorithm for automatic adaptive authoring in Augmented Reality for maintenance
    (Cranfield University, 2020-06-01 17:09) Fernández del amo blanco, Iñigo; Erkoyuncu, John ahmet; Farsi, Maryam
    This repository includes datasets on experimental cases of study and analysis regarding the research called "Programmable content and a pattern-matching algorithm for automatic adaptive authoring in Augmented Reality for maintenance".DOI:Abstract: "Augmented Reality (AR) can increase efficiency and safety of maintenance operations, but costs of augmented content creation (authoring) are hindering its industrial deployment. A relevant research gap involves the ability of authoring solutions to automatically generate content for multiple operations. Hence, this paper offers programmable content formats and a pattern-matching algorithm for automatic adaptive authoring of ontology -based maintenance data. The proposed solution is validated against common authoring tools for repair and remote diagnosis AR applications in terms of operational efficiency gains achieved with the content they produce. Experimental results show that content from all authoring solutions attain same time reductions (42%) in comparison with non-AR information delivery tools. Surveys results suggest alike perceived usability of all authoring solutions and better content adaptiveness and user’s performance tracking of this authoring proposal."
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    ItemOpen Access
    Datasets: Structured authoring for AR-based communication to enhance efficiency in remote diagnosis for complex equipment
    (Cranfield University, 2020-04-29 12:13) Fernández del amo blanco, Iñigo
    Datasets response variables and interaction factors have been described in https://doi.org/10.1016/j.aei.2020.101096