CORD - Cranfield University research data
Permanent URI for this community
In order to deposit your data, you will need to log in to this Deposit form.
Browse
Browsing CORD - Cranfield University research data by Author "ahmet Erkoyuncu, John"
Now showing 1 - 16 of 16
Results Per Page
Sort Options
Item Open Access Augmented Reality Training for Improved Learnability: data(Cranfield University, 2024-01-09 10:53) ahmet Erkoyuncu, JohnSurvey used Relevant images from the demonstrationItem Open Access Data for: An Effective Uncertainty Based Framework for Sustainable Industrial Product-Service System Transformation(Cranfield University, 2019-08-19 08:36) ahmet Erkoyuncu, JohnIndustrial Product-Service Systems (IPS2) can provide insights to enhance the environmental sustainability and lower environmental impact. However, its successful realisation for preventing the production of waste, while increasing efficiencies in the uses of energy and human capital remains a highly convoluted problem. This research article aims to address this issue by presenting an innovative uncertainty-based framework that can be used to assist in achieving increased sustainability within the context of IPS2. The developed framework explains the drivers for decision-making and cost to enable sustainability improvements in transforming to industrial services. This is based on academic literature, and multiple case studies of seven industrial companies with over 30 hours of semi-structured interviews. The validation of the framework through two case studies demonstrates that uncertainty management can enable resource efficiency and offer sustainable transformation to service provision. Files: one original Excel 2016 data file with macros (.xslm) for each case study; also, one pdf/a summary of data for both case studies.Item Open Access Data relating to: "An uncertainty quantification and aggregation framework for system performance assessment in industrial maintenance" (2020)(Cranfield University, 2021-02-03 11:26) Grenyer, Alex; ahmet Erkoyuncu, John; Addepalli, Pavan; Zhao, YifanExcel file corresponding to data in conference paper - tables summarising variables used in the paper, calculated in MATLABImages: Figures 1-4 as in conference paperPowerPoint presentationPublished paperItem Open Access Data relating to: "Compound uncertainty quantification and aggregation (CUQA) for reliability measurement in industrial maintenance" (2021)(Cranfield University, 2023-05-24 11:00) Grenyer, Alex; ahmet Erkoyuncu, John; Addepalli, Pavan; Zhao, YifanTables summarising variables used, calculated in MATLAB Images: Figures 1-4 as in the manuscript README.txt Excel file corresponding to data in the manuscriptItem Open Access Data relating to: "Current practice and challenges towards handling uncertainty for effective outcomes in maintenance" (2019)(Cranfield University, 2020-03-11 08:24) Grenyer, Alex; Dinmohammadi, Fateme; ahmet Erkoyuncu, John; Zhao, Yifan; Roy, RajkumarExcel file corresponding to data in conference paper:'Details' tab denotes participant experience and pedigree scores'Influencing factors' tab displays questionnaire results and analysis'Influencing factors w. pedigree' looks at how pedigree could be applied directly to questionnaire answers'Pairwise & AHP' shows construction and results of AHP processPowerPoint file: Embedded conference video presentation, summary of paper, comparison of approachesItem Open Access Data relating to: "Dynamic multistep uncertainty prediction in spatial geometry" (2020)(Cranfield University, 2021-02-12 11:48) Grenyer, Alex; Schwabe, Oliver; ahmet Erkoyuncu, John; Zhao, YifanExcel file corresponding to training data and results in conference paper - applied in MATLABImages: Figures 1-4 as in conference paperVideo: 3D plot rotationVideo: Conference presentationItem Open Access Data relating to: "Identifying challenges in quantifying uncertainty: case study in infrared thermography" (2018)(Cranfield University, 2020-03-11 08:24) Grenyer, Alex; Addepalli, Pavan; Zhao, Yifan; Oakey, Luke; ahmet Erkoyuncu, John; Roy, RajkumarExcel file corresponding to data in conference paper:'Paper tables' tab contains summary of variables used in the paper, calculated using MATLAB 'Conditions' tab contains recorded temperatures and humidity for each run read by MATLAB 'Readings' tab collates reading values for each run read by MATLAB'Run1-10' tabs contain data recorded for each run including ROI size and locationPowerPoint file: Conference presentationItem Open Access Data relating to: "Multistep prediction of dynamic uncertainty under limited data" (2022)(Cranfield University, 2022-01-24 12:52) Grenyer, Alex; Schwabe, Oliver; ahmet Erkoyuncu, John; Zhao, YifanExcel file: Symmetry trends used to establish correlation factorExcel file: Forecast method comparisonPowerPoint file: Figures in manuscriptPowerPoint file: Figures from additional simulationsMATLAB files to run the app and readme txt with instructionsItem Open Access Data supporting 'Hybrid recommendations and dynamic authoring for AR knowledge capture and re-use in diagnosis applications'(Cranfield University, 2023-02-16 11:10) ahmet Erkoyuncu, JohnIn Industry 4.0, integrated data management is an important challenge due to heterogeneity and the 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 it 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 the efficiency and effectiveness of their reporting tasks. The proposed contributions have been validated 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 the 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.Item Open Access 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, RajkumarThis 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.Item Open Access Data: Fast Augmented Reality Authoring: Fast Creation of AR step-by-step Procedures for Maintenance Operations(Cranfield University, 2023-08-14 14:15) Palmarini, Riccardo; Fernandez Del Amo Blanco, Inigo; Ariansyah, Dedy; Khan, Samir; ahmet Erkoyuncu, John; Roy, RajkumarAugmented Reality (AR) has shown great potential for improving human performance in Maintenance, Repair, and Overhaul (MRO) operations. Whilst most studies are currently being carried out at an academic level, the research is still in its infancy due to limitations in three main aspects: limited hardware capabilities, the robustness of object recognition, and content-related issues. This article focuses on the last point, by proposing a new geometry-based method for creating a step-by-step AR procedure for maintenance activities. The Fast Augmented Reality Authoring (FARA) method assumes that AR can recognise and track all the objects in a maintenance environment when CAD models are available, to knowledge transfer to a non-expert maintainer. The novelty here lies in the fact that FARA is a human-centric method for authoring animation-based procedures with minimal programming skills and the manual effort required. FARA has been demonstrated, as a software unit, in an AR system composed of commercially available solutions and tested with over 30 participants. The results show an average time saving of 34.7% (min 24.7%; max 55.3%) and an error reduction of 68.6% when compared to the utilisation of traditional hard-copy manuals. Comparisons are also drawn from performances of similar AR applications to illustrate the benefits of procedures created utilising FARA.Item Open 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, MaryamThis 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.”Item Open 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, StephenThis 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."Item Open Access Olfactory-based Augmented Reality Support for Industrial Maintenance(Cranfield University, 2020-01-14 16:19) ahmet Erkoyuncu, JohnAugmented reality (AR) applications have opened innovative ways for performance improvement in the IoT industry. It can enhance user perception of the real-world by providing valuable information about an industrial environment and provide visual virtual information onto a head-mounted device (HMD). Such information is important for maintainers to quickly detect abnormalities, reduce nugatory routinesand facilitate preventive maintenance activities. Since odors are made up of volatile compounds at low concentration, they can be used for olfactory-based identification. The article outlines the development an olfactory-based AR system to help with the identification of maintenance issues using smell. The prototype comprises of three components: an electronic nose, a database and an AR application integrated with Microsoft HoloLens. After diagnosing an odor, the data is sent wirelessly through a local network to the HMD worn by the user. To validate the technology, four odors have been used, including engine oil, sun lotion, medical alcohol and perfume, to record behaviors and demonstrate the repeatability of the process. The presented technology incorporates sampling methods, cleaning processes and statistical analysis that can be further scrutinized to allow better smell augmentation for diagnosis.Item Open Access 'Quantifying uncertainty in pulsed thermography inspection by analysing the thermal diffusivity measurements of metals and composites - Dataset to reconstruct the results presented in the paper'(Cranfield University, 2021-08-14 10:40) Addepalli, Pavan; Zhao, Yifan; ahmet Erkoyuncu, John; Roy, Rajkumar'This is the underlying dataset for the paper based on which uncertainty quantification was carried out.'Item Open Access Supporting data for "A framework to estimate the cost of No-Fault Found events"(Cranfield University, 2017-10-10 11:57) ahmet Erkoyuncu, John; Roy, Rajkumar; Khan, Samir; Mohammed Fazal Hussain, SyedThe files cover: the questionnaire used, the input data requirements, and a presentation containing further data and supporting information that went into the modelling in the paper "A framework to estimate the cost of No-Fault Found events".