CERES :: Browsing by Author "Erkoyuncu, John Ahmet"

Browsing by Author "Erkoyuncu, John Ahmet"

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Open Access
Additive manufacturing applications in Defence Support Services: current practices and framework for implementation
(Springer Verlag (Germany), 2017-02-21) Busachi, Alessandro; Erkoyuncu, John Ahmet; Colegrove, Paul A.; Drake, R.; Watts, C.; Wilding, S.
This research investigates through a systems approach, “Additive Manufacturing” (AM) applications in “Defence Support Services” (DS2). AM technology is gaining increasing interest by DS2 providers, given its ability of rapid, delocalised and flexible manufacturing. From a literature review and interviews with industrial and academic experts, it is apparent that there is a lack of research on AM applications in DS2. This paper’s contribution is represented by the following which has been validated extensively by industrial and academic experts: (1) DS2 current practices conceptual models, (2) a framework for AM implementation and (3) preliminary results of a next generation DS2 based on AM. To carry out the research, a Soft System Methodology was adopted. Results from the research increased the confidence of the disruptive potential of AM within the DS2 context. The main benefits outlined are (1) an increased support to the availability given a reduced response time, (2) reduced supply chain complexity given only supplies of raw materials such as powder and wire, (3) reduced platform inventory levels, providing more space and (4) reduced delivery time of the component as the AM can be located near to the point of use. Nevertheless, more research has to be carried out to quantify the benefits outlined. This requirement provides the basis for the future research work which consists in developing a software tool (based on the framework) for experimentation purpose which is able to dynamically simulate different scenarios and outline data on availability, cost and time of service delivered.
Open Access
Advanced uncertainty quantification with dynamic prediction techniques under limited data for industrial maintenance applications.
(Cranfield University, 2021-07) Grenyer, Alex; Erkoyuncu, John Ahmet; Zhao, Yifan
Engineering systems are expected to function effectively whilst maintaining reliability in service. These systems consist of various equipment units, many of which are maintained on a corrective or time-based basis. Challenges to plan maintenance accounting for turnaround times, equipment availability and resulting costs manifest varying degrees of uncertainty stemming from multiple quantitative and qualitative (compound) sources throughout the in-service life. Under or over-estimating this uncertainty can lead to increased failure rates or, more often, unnecessary maintenance being carried out. As well as the quality availability of data, uncertainty is driven by the influence of expert experience or assumptions and environmental operating conditions. Accommodating for uncertainty requires the determination of key contributors, their influence on interconnected units and how this might change over time. This research aims to develop a modelling approach to quantify, aggregate and forecast uncertainty given by a combination of historic equipment data and heuristic estimates for in-service engineering systems. Research gaps and challenges are identified through a systematic literature review and supported by a series of surveys and interviews with industrial practitioners. These are addressed by the development of two frameworks: (1) quantify and aggregate compound uncertainty, and (2) predict uncertainty under limited data. The two frameworks are brought together to produce the Multistep Compound Dynamic Uncertainty Quantification (MCDUQ) app, developed in MATLAB. Results demonstrate effective measurement of compound uncertainties and their impact on system reliability, along with robust predictions under limited data with an immersive visualisation of dynamic uncertainty. The embedded frameworks are each validated through implementation in two case studies. The app is verified with industrial experts through a series of interviews and virtual demonstrations.
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
An agent-based approach to quantify the uncertainty in product-service system contract decisions: a case study in the machine tool industry
(Elsevier, 2020-12-25) Farsi, Maryam; Erkoyuncu, John Ahmet
Product-service system (PSS) business models appraise the relationship between different stakeholders and focus on a partnership based on profit. Existing literature discusses servitization and the associated cost-benefit analysis (CBA) models mostly from the perspective of original equipment manufacturers. Additionally, CBA is typically conducted using top-down approaches and standard activity-based costing, with limited available data and without considering uncertainty. As a result, inadequate and under-priced contract decisions may be made. To address the problem, this paper extends the current literature by proposing a novel framework for quantifying uncertainty in cost and benefit estimates of PSS contracts. The framework offers a bottom-up costing approach using the agent-based simulation technique. The framework comprises a stochastic CBA model for PSS. It is developed by considering through-life cost and benefit of products and services with aggregate uncertainty in terms of service costs, service lead-times, and their occurrences. The framework has been tested successfully on a real-world case study with a bespoke service provider in the machine tool industry. The model is applied to include spare-parts and availability-based servitization contracts. The simulation results are validated by real-world measurements and expert knowledge. The results involve a comprehensive stochastic analyses of a through-life CBA under probabilistic uncertainty and provide the opportunity to quantify the uncertainty in PSS contract decisions. Moreover, the results highlight that servitization is more beneficial for bespoke service providers in long-term contracts, and for relatively new or retrofitted products. Further research works are required to apply the model on capability-based contracts
Open Access
An agent-based model for flexible customization in product-service systems
(Elsevier, 2021-02-10) Farsi, Maryam; Erkoyuncu, John Ahmet
Product-Service System (PSS) models offer an integrated service solution to create value for businesses. In the high-value manufacturing sector, value creation for maintaining market competitiveness and improving customer satisfaction is a challenging task. Designing an effective PSS solution depends on integrated service, and product requirements and constraints. Thereby, PSS contract decisions can be significantly influenced by customers’ requirements, and also product and service features. However, existing literature primarily focuses on the impact of service requirements on the PSS contract decisions. Moreover, the existing insights for PSS customization mainly consider hysteretic customer requirements rather than forecasting the requirements under product and service uncertainties. In this paper, an agent-based cost-benefit analysis simulation model is implemented for the PSS contract decisions context. Moreover, a sensitivity analysis is conducted on service costs. Additionally, the effect of product remaining life on service contract decisions is analyzed. The simulation model considers stochastic uncertainty to study PSS contracts customization. The presented model supports PSS customization process by providing a quantitative tool that measures contracts’ profitability as early as the requirement elicitation phase. Furthermore, the bottom-up nature of the model, and the integration of probabilistic uncertainties enhance the flexibility of PSS customization. A case study of PSS contract decision in the machine tool industry is considered for assessing the validity of the presented model. Studies on different forms of service uncertainty highlight that the product failure rate has the most influence on the profitability of a service contract. Moreover, the impact of product age on profitability in an availability-based contract is more significant compared to a spare-parts contract.
Open Access
Application of CNN for multiple phase corrosion identification and region detection
(Elsevier BV, 2024-10-30) Oyedeji, Oluseyi Ayodeji; Khan, Samir; Erkoyuncu, John Ahmet
Corrosion is a significant issue that contributes negatively to the degradation of materials most especially metals. To ensure proper maintenance, enhance reliability and prevent breakdown, it is very essential to not only effectively detect corrosion but to also understand its locations and distributions on the materials. A Multiple phase Convolutional Neural Network (CNN) model is created for this purpose. The Multiple phase CNN model consists of custom designed deep learning algorithms at various stages. This created the opportunity to make use of binary classification, multi-label classification and patch distribution algorithm to detect and identify corrosion regions on metallic materials. Six (6) different labels of corrosion were modelled to represent different levels of degradation using 600 anonymized images. The images were used in the various stages of the framework for training the respective models. Results at the binary level shows 94.87 % of corrosion detection. The multiclass stage of the Multiple phase CNN records the highest accuracy of 92.1 %. The patch distribution stage recorded a highest accuracy of 96.5 % and 94.6 % for the Average Image and Average Pixel ROCAUC (Region of Concentration Area Under Cover). It also shows a region segment average accuracy detection of 91.5 % (image level) and 89.2 %(pixel level) for 9 distinct regions. The research provides a comprehensive and detailed reliability and maintenance information for Aerospace, Transport and Manufacturing Materials experts and non-experts. The framework shows a robust approach to detecting corrosion which is essential for critical and safety applications as well as preventing economic loss due to corrosion. This can also be extended to other domains beyond the corrosion case study.
Open Access
An approach for selecting cost estimation techniques for innovative high value manufacturing products
(Elsevier, 2016-11-02) Schwabe, Oliver; Shehab, Essam; Erkoyuncu, John Ahmet
This paper presents an approach for determining the most appropriate technique for cost estimation of innovative high value manufacturing products depending on the amount of prior data available. Case study data from the United States Scheduled Annual Summary Reports for the Joint Strike Fighter (1997-2010) is used to exemplify how, depending on the attributes of a priori data certain techniques for cost estimation are more suitable than others. The data attribute focused on is the computational complexity involved in identifying whether or not there are patterns suited for propagation. Computational complexity is calculated based upon established mathematical principles for pattern recognition which argue that at least 42 data sets are required for the application of standard regression analysis techniques. The paper proposes that below this threshold a generic dependency model and starting conditions should be used and iteratively adapted to the context. In the special case of having less than four datasets available it is suggested that no contemporary cost estimating techniques other than analogy or expert opinion are currently applicable and alternate techniques must be explored if more quantitative results are desired. By applying the mathematical principles of complexity groups the paper argues that when less than four consecutive datasets are available the principles of topological data analysis should be applied. The preconditions being that the cost variance of at least three cost variance types for one to three time discrete continuous intervals is available so that it can be quantified based upon its geometrical attributes, visualised as an n-dimensional point cloud and then evaluated based upon the symmetrical properties of the evolving shape. Further work is suggested to validate the provided decision-trees in cost estimation practice.
Open Access
Augmented reality assisted calibration of digital twins of mobile robots
(Elsevier, 2020-12-18) Williams, Richard; Erkoyuncu, John Ahmet; Masood, Tariq; Vrabič, Rok
In this age of globalisation and digitalisation, industry is evolving from a physical space information flow towards a two-way communication between virtual and physical space. The challenge that this research aims to resolve is: ‘how can a virtual system adjust itself to the constantly changing conditions of the physical space of information that influences the operational dynamics of maintenance in industry?’. This article presents an augmented reality (AR) assisted digital twin (DT) solution that can be used to calibrate mobile robots in maintenance environments. This DT solution was achieved by providing the user the ability to predict the battery charge of the mobile robot by using historic data as the input and providing the user a visual representation of the mobile robot’s movements using an AR device as a medium to display this digital data. Overall, the trial demonstration was a success in implementing a DT to calibrate a mobile robot with AR assistance. Therefore, this DT solution can be implemented into niche areas of industrial environments. With the capability of predicting the battery charge enabling the user to know when the mobile robot will be empty, the user can maximise its use before recalling it for the charge. This would improve the accuracy of scheduling when mobile robots can be deployed and maximise the utilization of the robot and reduce the running cost of mobile robots in the long term
Open Access
Augmented Reality in Maintenance: An information-centred design framework
(Elsevier, 2018-02-08) Fernández del Amo, Iñigo; Erkoyuncu, John Ahmet; Roy, Rajkumar; Wilding, Stephen
Augmented Reality (AR) visualization capabilities can impact on maintenance. From enhancing performance to retrieving feedback, AR can close the information loop between maintenance information systems and the operations supported. Though, the design of AR applications is not aligned with current information systems, which prevents maintenance information to be used and improved properly. In this paper, industrial collaboration contributed to determine a framework for AR integration in maintenance systems. The framework describes information types, formats and interactions modes for AR to enhance efficiency improvements in maintenance of complex equipment. Semi-structured interviews and surveys with maintainers were conducted to determine the maintenance challenges and also to validate the framework proposed. Therefore, exposing future research in topics such as multimodal interaction, information contextualization and performance analysis to achieve the complete integration of AR in maintenance.
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
Authoring digital contents for augmented reality in maintenance.
(2018-04) Palmarini, Riccardo; Erkoyuncu, John Ahmet; Roy, Rajkumar
Technicians’ performance is a major driver in maintenance and each process can be prone to time and quality variances as well as errors due to factors such as experience, complexity and environment. Augmented Reality (AR) is an emerging technology that has been applied in a wide variety of disciplines and has been demonstrated to have a role with improving efficiency, effectiveness and decision-making within industrial maintenance. AR has not reached its full potential yet and its implementation in Industry is slowed down by three main limitations: hardware restricted capabilities, object recognition robustness and contents-related issues. This PhD project focuses on easing the implementation of AR by overcoming the AR technology selection challenges and the AR contents-related issues. In order to reach the aim, the student has provided three main contributions to knowledge: 1) a process to select AR technology for maintenance (IPSAR), 2) a method for creating AR step-by-step procedures (FARP) and 3) a method for providing remote assistance (ARRA). FARP and ARRA methods have been developed and tested. The first allows recording procedures in an ad-hoc designed “AR-format” and is able to show “step-by-step” procedures. It aims to support deskilling the maintenance process and reducing the error rate by simplifying the delivery of maintenance with efficient and effective guidance. The second overcomes current remote video-call assistance limitations by improving spatial referencing. ARRA module allows to provide AR-assistance by overlaying virtual objects on the real environment of a remote maintainer. The methods proposed by the student could boost the implementation of AR and open the doors for a bright future in which AR supports technicians thus reducing operational costs and training and improving human performances.
Open Access
Automation of knowledge extraction for degradation analysis
(Elsevier, 2023-07-13) Addepalli, Sri; Weyde, Tillman; Namoano, Bernadin; Oyedeji, Oluseyi Ayodeji; Wang, Tiancheng; Erkoyuncu, John Ahmet; Roy, Rajkumar
Degradation analysis relies heavily on capturing degradation data manually and its interpretation using knowledge to deduce an assessment of the health of a component. Health monitoring requires automation of knowledge extraction to improve the analysis, quality and effectiveness over manual degradation analysis. This paper proposes a novel approach to achieve automation by combining natural language processing methods, ontology and a knowledge graph to represent the extracted degradation causality and a rule based decision-making system to enable a continuous learning process. The effectiveness of this approach is demonstrated by using an aero-engine component as a use-case.
Open Access
Brain functional and effective connectivity based on electroencephalography recordings: a review
(Wiley, 2021-10-20) Cao, Jun; Zhao, Yifan; Shan, Xiaocai; Wei, Hua-Liang; Guo, Yuzhu; Chen, Liangyu; Erkoyuncu, John Ahmet; Sarrigiannis, Ptolemaios Georgios
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.
Open Access
Cognitive data imputation: case study in maintenance cost estimation
(Elsevier, 2023-07-13) Erkoyuncu, John Ahmet; Namoano, Bernadin; Kozjek, Dominik; Vrabič, Rok
Cost estimation is critical for effective decision making in engineering projects. However, it is often hampered by a lack of sufficient data. For this, data imputation techniques can be used to estimate missing costs based on statistical estimates or analogies with historical data. However, these techniques are often limited because they do not consider the existing knowledge of experts. In this paper, a novel cognitive data imputation technique is proposed for cost estimation that uses explanatory interactive machine learning to integrate and improve human knowledge. Through a case study in maintenance cost estimation the effectiveness of the approach is demonstrated.
Open Access
Cognitive digital twin: an approach to improve the maintenance management
(Elsevier, 2022-06-23) D’Amico, Rosario Davide; Erkoyuncu, John Ahmet; Addepalli, Sri; Penver, Steve
Digital twin (DT) technology allows the user to monitor the asset, specifically over the operation and service phase of the life cycle, which is the longest-lasting phase for complex engineering assets. This paper aims to present a thematic review of DTs in terms of the technology used, applications, and limitations specifically in the context of maintenance. This review includes a systematic literature review of 59 articles on semantic digital twins in the maintenance context. Key performance indicators and explanations of the main concepts constituting the DT have been presented. This article contains a description of the evolution of DTs together with their characterisation for maintenance purposes. It provides an ontological approach to develop DT and improve the maintenance management leading to the creation of a structured DT or a Cognitive Twin (CT). Moreover, it points out that using a top-level ontology approach should be the starting point for the creation of CT. Enabling the creation of the digital framework that will break down silos, ensuring a perfect integration in a network of twins’ scenario.
Open Access
Compound uncertainty quantification and aggregation (CUQA) for reliability assessment in industrial maintenance
(MDPI, 2023-05-16) Grenyer, Alex; Erkoyuncu, John Ahmet; Addepalli, Sri; Zhao, Yifan
The mounting increase in the technological complexity of modern engineering systems requires compound uncertainty quantification, from a quantitative and qualitative perspective. This paper presents a Compound Uncertainty Quantification and Aggregation (CUQA) framework to determine compound outputs along with a determination of the greatest uncertainty contribution via global sensitivity analysis. This was validated in two case studies: a bespoke heat exchanger test rig and a simulated turbofan engine. The results demonstrated the effective measurement of compound uncertainty and the individual impact on system reliability. Further work will derive methods to predict uncertainty in-service and the incorporation of the framework with more complex case studies.
Open Access
A conceptual design for smell based augmented reality: case study in maintenance diagnosis
(Elsevier, 2018-11-24) Wang, Jeff; Erkoyuncu, John Ahmet; Roy, Rajkumar
The trend of Industry 4.0 encourages the next generation of manufacturing to be flexible, intelligent, and interoperable. The implementations of the Artificial Intelligence (AI) technology could potentially enhance maintenance in efficiency, and accuracy. However, it will not be a substitution to the human operator’s flexibility, decision-making and information received by the natural five senses. Augmented reality (AR) is commonly understood as a technology that overlays virtual information onto the existing environment to provide users a new and improved experience to assist their daily activities. However, AR can be used to enhance all human five senses rather than just overlay virtual imagery. In this paper, a design and a practical plan of smell augmentation for diagnosis is initialised, via a case study in maintenance. The aim of this paper is to evaluate the feasibilities, identify challenges, and summarise initial results of overlaying information through smell augmentations.
Open Access
A conceptual framework to assess the impact of training on equipment cost and availability in the military context
(Elsevier, 2015-10-27) Rodrigues, Duarte; Erkoyuncu, John Ahmet; Starr, Andrew G.; Wilding, Steve; Dibble, Alan; Laity, Martin; Owen, Richard
Designing military support is challenging and current practices need to be reviewed and improved. This paper gives an overview of the Industry current practices in designing military support under Ministry of Defence/Industry agreements (in particular for Contracting for Availability (CfA)), and identifies challenges and opportunities for improvement. E.g. training delivery was identified as an important opportunity for improving the CfA in-service phase. Thus, an innovative conceptual framework is presented to assess the impact of training on the equipment availability and cost. Additionally, guidelines for improving the current training delivery strategies are presented, which can also be applied to other Industry contexts.
Open Access
Conceptualising the impact of information asymmetry on through-life cost: case study of machine tools sector
(Elsevier, 2019-11-02) Farsi, Maryam; Grenyer, Alex; Sachidananda, Madhu; Sceral, Mario; Mcvey, Steve; Erkoyuncu, John Ahmet; Roy, Rajkumar
Information asymmetry (IA) in terms of contextual variety and importance is one of the most challenging aspects of through-life costing in product-service systems (PSS). IA is an imbalance in the information, data and knowledge shared among the parties involved in a contractual agreement. In manufacturing systems under PSS, interaction and effective communication among several parties who are involved in a contractual agreement, rely on the continuity and accuracy of information and context. In such systems, contextual variety exhibits complexity and uncertainty in through-life costing and subsequently in PSS cost assessment. Although the economic aspect of PSS has been studied previously, the impact of IA on through-life cost and for different PSS solutions has not been detailed. Considering manufacturing value chains, this paper introduces a new concept of PSS-hierarchy to perform through-life costing in the presence of IA for various PSS solutions. Moreover, this paper proposes a generic life-cycle model for different PSS solutions to assess the total cost of ownership (TCO). The proposed model has been developed to support decisions on contract design in manufacturing systems. This study considers the manufacturer, service provider and customer perspectives to develop the TCO model using a machine tool manufacturing case study.
Open Access
A cost estimation approach for IoT modular architectures implementation in legacy systems
(Elsevier, 2018-02-08) Tedeschi, Stefano; Emmanouilidis, Christos; Erkoyuncu, John Ahmet; Rodrigues, Duarte Polonia; Roy, Rajkumar; Starr, Andrew
Industry 4.0 has encouraged manufacturing organisations to update their systems and processes by implementing Internet of Things (IoT) technology in legacy systems to provide new services such as autonomous condition monitoring and remote maintenance. However, there is still no literature that guides in realizing the advantages and disadvantages of the fourth industry revolution in terms of complexity, data security, and cost. This paper lays the foundation for the creation of an innovative conceptual model to estimate the cost for implementation of new architectures for legacy systems. The proposed approach considers aspects that impact the cost of different IoT architectures such as: complexity, data gathering and sharing protocols, and cyber security. The authors suggest a further implementation of the cost model, in order to guide the organisations in the most cost-effective architecture for modernisation of their legacy systems.