CERES :: Browsing by Author "Ariansyah, Dedy"

Browsing by Author "Ariansyah, Dedy"

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Open Access
A head mounted augmented reality design practise for maintenance assembly
(Cranfield University, 2021-08-18 17:03) Ariansyah, Dedy
This is the experimental data used to examine the impact of different AR HMD modalities on task performance, system usability, and user safety
Open Access
Advancing fault diagnosis through ontology-based knowledge capture and application
(IEEE, 2024-07-25) Del Amo, Iñigo Fernández; Erkoyuncu, John Ahmet; Bulka, Dominik; Farsi, Maryam; Ariansyah, Dedy; Khan, Samir; Wilding, Stephen
This article addresses a critical gap in the field of fault diagnosis for complex systems, focusing on the development and application of an ontology-based approach to capture and utilize expert knowledge. The key objective is to enhance fault diagnosis precision and effectiveness, specifically in challenging No-Fault-Found (NFF) scenarios, by harnessing the extensive, often implicit, understanding of seasoned professionals. The study uses a comprehensive methodology that includes creating a specialized ontology called DIAGONT, which captures the expert reasoning in fault diagnosis. Field experts contribute to the development of this ontology, ensuring its relevance and applicability. Real-world case studies and controlled experiments are used to rigorously validate the ontology. The goal of these experiments is to evaluate how effective the ontology is in enhancing fault diagnosis procedures when compared to traditional methods. Our case studies focused on two complex engineering assets, a loading arm and a helicopter mission system, due to their complexity and the frequency of non-functional failure scenarios. The analysis shows that using the DIAGONT ontology leads to improved accuracy and efficiency in fault diagnosis. A structured format allowed experts to successfully capture and reuse diagnostic knowledge, resulting in a noticeable reduction in NFF scenarios. The application of ontology-based approach exhibited potential in enhancing knowledge transfer between experts and less experienced technicians, potentially resulting in long-lasting improvements in maintenance practices. The results highlight how ontology-based systems can improve fault diagnosis in complex engineering systems.
Open Access
Analysis of autonomic indexes on drivers' workload to assess the effect of visual ADAS on user experience and driving performance in different driving conditions
(ASME, 2018-06-12) Ariansyah, Dedy; Caruso, Giandomenico; Ruscio, Daniele; Bordegoni, Monica
Advanced driver assistance systems (ADASs) allow information provision through visual, auditory, and haptic signals to achieve multidimensional goals of mobility. However, processing information from ADAS requires operating expenses of mental workload that drivers incur from their limited attentional resources. The change in driving condition can modulate drivers' workload and potentially impair drivers' interaction with ADAS. This paper shows how the measure of cardiac activity (heart rate and the indexes of autonomic nervous system (ANS)) could discriminate the influence of different driving conditions on drivers' workload associated with attentional resources engaged while driving with ADAS. Fourteen drivers performed a car-following task with visual ADAS in a simulated driving. Drivers' workload was manipulated in two driving conditions: one in monotonous condition (constant speed) and another in more active condition (variable speed). Results showed that drivers' workload was similarly affected, but the amount of attentional resources allocation was slightly distinct between both conditions. The analysis of main effect of time demonstrated that drivers' workload increased over time without the alterations in autonomic indexes regardless of driving condition. However, the main effect of driving condition produced a higher level of sympathetic activation on variable speed driving compared to driving with constant speed. Variable speed driving requires more adjustment of steering wheel movement (SWM) to maintain lane-keeping performance, which led to higher level of task involvement and increased task engagement. The proposed measures appear promising to help designing new adaptive working modalities for ADAS on the account of variation in driving condition.
Open Access
Augmented reality training for improved learnability
(Elsevier, 2023-12-06) Ariansyah, Dedy; Pardamean, Bens; Barbaro, Eddine; Erkoyuncu, John Ahmet
In the current era of Industry 4.0, many new technologies offer manufacturing industries to achieve high productivity. Augmented Reality (AR) is one of the emerging technologies that has been adopted in industries to aid users in acquiring complex skills and carrying out many complicated tasks such product assembly and maintenance. Nevertheless, most AR applications have been developed without clear understanding of how such technology can facilitate improved learnability in terms of knowledge reusability. This paper proposed an enhanced AR-based training system that provides multimodal information with a contextualized information to improve task comprehension and knowledge reusability compared with traditional AR that presents unimodal and decontextualized information. An empirical test was carried out to assess the task performance and the task learnability aspects of this enhanced AR compared to the traditional AR and the paper-based document. The experiment consisted of a training phase where participants carried out an electrical connection task of a sensor followed by a knowledge reuse phase where participants had to wire a second sensor using their previous training. A pre-test quiz was given before the experiment followed by the post-tests phase after the training. Post-tests consist of one post-test given directly after the experiment (short-term retention test) and a second post-test quiz given one week later (long-term retention test) to measure information retention. The results indicated that AR-based approaches could enhance knowledge acquisition by around 18 % for traditional AR and almost 25 % for enhanced AR as compared to paper-based approach. While all training systems achieved relatively equivalent well for short-term retention test, trainees who used the enhanced AR training systems statistically outperformed those in the paper-based group for long term retention test. Furthermore, there was a positive correlation between the score of short-term retention test and the score in the knowledge reusability which was also shown by the higher scores in knowledge reusability for the enhanced AR training system compared to the other two approaches. These findings are discussed in relation to the Industry 5.0′s human centric core value.
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, 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.
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, Rajkumar
Augmented 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.
Open Access
A design framework for adaptive digital twins
(Elsevier, 2020-05-20) Erkoyuncu, John Ahmet; Fernández del Amo, Iñigo; Ariansyah, Dedy; Bulka, Dominik; Vrabič, Rok; Roy, Rajkumar
Digital Twin (DT) is a ‘living’ entity that offers potential with monitoring and improving functionality of interconnected complex engineering systems (CESs). However, lack of approaches for adaptively connecting the existing brownfield systems and their data limits the use of DTs. 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.
Open Access
A digital twin architecture for effective product lifecycle cost estimation
(Elsevier, 2021-06-02) Farsi, Maryam; Ariansyah, Dedy; Erkoyuncu, John Ahmet; Harrison, Andrew
Lifecycle cost estimation is crucial for high-value manufacturing sectors, in particular at the early product design stage, to maintain their product affordability and manufacturing profitability within the market. Accordingly, it is important to identify through-life cost reduction opportunities. However, this is a challenging task for designers at the early product lifecycle stage due to the lack of complete historical data and the existence of high-level uncertainties within the product and service cost data. Moreover, the complexity of maintenance, repair, and overhaul interventions during the operation stage reduces the designers’ decision-making confidence level at the earlier stages. This paper aims to address these challenges by proposing a novel Digital Twin (DT) architecture that uses adaptive data structure and ontologies to automatically produce the cost model from data mined information throughout a product lifecycle. The DT architecture supports designers by capturing data in terms of consumed and caused cost and automates the data flow to provide an adaptive cost estimation method across the product lifecycle. The DT enables designers to estimate the lifecycle cost at the early stage and to identify the through-life cost reduction opportunities effectively. Thereby, it is expected that the proposed DT supports OEMs to reduce the total lifecycle cost and improve the efficiency of their product development. A case study of lifecycle cost estimation in the machine tool industry is considered for testing the validity of the DT architecture.
Open Access
Fast augmented reality authoring: fast creation of AR step-by-step procedures for maintenance operations
(IEEE, 2023-01-24) Palmarini, Riccardo; Fernández del Amo, Iñigo; Ariansyah, Dedy; Khan, Samir; Erkoyuncu, John Ahmet; Roy, Rajkumar
Augmented 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.
Open Access
A head mounted augmented reality design practice for maintenance assembly: toward meeting perceptual and cognitive needs of AR users
(Elsevier, 2021-09-28) Ariansyah, Dedy; Erkoyuncu, John Ahmet; Eimontaite, Iveta; Johnson, Teegan; Oostveen, Anne-Marie; Fletcher, Sarah; Sharples, Sarah
Head Mounted Display (HMD) based Augmented Reality (AR) is being increasingly used in manufacturing and maintenance. However, limited research has been done to understand user interaction with AR interfaces, which may lead to poor usability, risk of occupational hazards, and low acceptance of AR systems. This paper uses a theoretically-driven approach to interaction design to investigate the impact of different AR modalities in terms of information mode (i.e. video vs. 3D animation) and interaction modality (i.e. hand-gesture vs. voice command) on user performance, workload, eye gaze behaviours, and usability during a maintenance assembly task. The results show that different information modes have distinct impacts compared to paper-based maintenance, in particular, 3D animation led to a 14% improvement over the video instructions in task completion time. Moreover, insights from eye gaze behaviours such as number of fixations and transition between Areas of Interest (AOIs) revealed the differences in attention switching and task comprehension difficulty with the choice of AR modalities. While, subjective user perceptions highlight some ergonomic issues such as misguidance and overreliance, which must be considered and addressed from the joint cognitive systems’ (JCSs) perspective and in line with the predictions derived from the Multiple Resources Model.
Open Access
Hybrid recommendations and dynamic authoring for AR knowledge capture and re-use in diagnosis applications
(Elsevier, 2021-12-18) Fernández del Amo, Iñigo; Erkoyuncu, John Ahmet; Farsi, Maryam; Ariansyah, Dedy
In 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.
Open Access
An intelligent agent-based architecture for resilient digital twins in manufacturing
(Elsevier, 2021-06-11) Vrabič, Rok; Erkoyuncu, John Ahmet; Farsi, Maryam; Ariansyah, Dedy
Digital twins (DTs) offer the potential for improved understanding of current and future manufacturing processes. This can only be achieved by DTs consistently and accurately representing the real processes. However, the robustness and resilience of the DT itself remain an issue. Accordingly, this paper offers an approach to deal with uncertainty and disruptions, as the DT detects these effectively and self-adapts as needed to maintain representativeness. The paper proposes an intelligent agent-based architecture to improve the robustness (including accuracy of representativeness) and resilience (including timely update) of the DT. The approach is demonstrated on a case of cryogenic secondary manufacturing
Open Access
A unified framework for digital twin development in manufacturing
(Elsevier, 2024-05-04) Latsou, Christina; Ariansyah, Dedy; Salome, Louis; Erkoyuncu, John Ahmet; Sibson, Jim; Dunville, John
The concept of digital twin (DT) is undergoing rapid transformation and attracting increased attention across industries. It is recognised as an innovative technology offering real-time monitoring, simulation, optimisation, accurate forecasting and bi-directional feedback between physical and digital objects. Despite extensive academic and industrial research, DT has not yet been properly understood and implemented by many industries, due to challenges identified during its development. Existing literature shows that there is a lack of a unified framework to build DT, a lack of standardisation in the development, and challenges related to coherent goals of DT in a multi-disciplinary team engaged in the design, development and implementation of DT to a larger scale system. To address these challenges, this study introduces a unified framework for DT development, emphasising reusability and scalability. The framework harmonises existing DT frameworks by unifying concepts and process development. It facilitates the integration of heterogeneous data types and ensures a continuous flow of information among data sources, simulation models and visualisation platforms. Scalability is achieved through ontology implementation, while employing an agent-based approach, it monitors physical asset performance, automatically detects faults, checks repair status and offers operators feedback on asset demand, availability and health conditions. The effectiveness of the proposed DT framework is validated through its application to a real-world case study involving five interconnected air compressors located at the Connected Facility at Devonport Royal Dockyard, UK. The DT automatically and remotely monitors the performance and health status of compressors, providing guidance to humans on fault repair. This guidance dynamically adapts based on feedback from the DT. Analyses of the results demonstrate that the proposed DT increases the facility’s operation availability and enhances decision-making by promptly and accurately detecting faults.

Browsing by Author "Ariansyah, Dedy"

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