CERES :: Browsing by Author "Emmanouilidis, Christos"

Browsing by Author "Emmanouilidis, Christos"

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Open Access
A Design Approach to IoT Endpoint Security for Production Machinery Monitoring
(Cranfield University, 2019-05-22 16:09) Tedeschi, Stefano; Emmanouilidis, Christos; Mehnen, Jörn; Roy, Rajkumar
The Internet of Things (IoT) has significant potential in upgrading legacy production machinery with monitoring capabilities to unlock new capabilities and bring economic benefits. However, the introduction of IoT at the shop floor layer exposes it to additional security risks with potentially significant adverse operational impact. This project addresses such fundamental new risks at their root by introducing a novel endpoint security by design approach. The approach is implemented on a widely applicable production machinery monitoring application by introducing real time adaptation features for IoT device security through subsystem isolation and a dedicated lightweight authentication protocol.
Open Access
Change detection in streaming data analytics: a comparison of Bayesian online and martingale approaches
(Elsevier, 2020-12-18) Namoano, Bernadin; Emmanouilidis, Christos; Ruiz-Carcel, Cristobal; Starr, Andrew G.
On line change detection is a key activity in streaming analytics, which aims to determine whether the current observation in a time series marks a change point in some important characteristic of the data, given the sequence of data observed so far. It can be a challenging task when monitoring complex systems, which are generating streaming data of significant volume and velocity. While applicable to diverse problem domains, it is highly relevant to monitoring high value and critical engineering assets. This paper presents an empirical evaluation of two algorithmic approaches for streaming data change detection. These are a modified martingale and a Bayesian online detection algorithm. Results obtained with both synthetic and real world data sets are presented and relevant advantages and limitations are discussed.
Open Access
Context ontology development for connected maintenance services
(Elsevier, 2020-11-30) Emmanouilidis, Christos; Gregori, Matteo; Al-Shdifat, Ali
The opportunity to shift from corrective and preventive to data-driven Predictive Maintenance has received a significant boost with the deeper penetration of Internet of Things (IoT) technologies in industrial environments. Processing IoT generated data nonetheless creates challenges for data management and actionable data processing. One way to handle such complexity is to introduce context information modelling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. In this paper, context information management is considered on the basis of a valid knowledge construct for reliability-oriented maintenance management. The aim is to produce a viable semantic organization of data for maintenance services. It is applied on an industrial case linked to maintenance of a distributed fleet of connected production grade industrial printers. The complexity of translating the data generated by such production assets to actionable information is significant, as the status of a single asset is characterised by several hundreds of failure modes and a multitude of event codes. To assess the viability of the ontology for the targeted application, a qualitative usability evaluation study of the ontology is performed.
Open Access
Context-based and human-centred information fusion in diagnostics
(Elsevier, 2016-12-16) Emmanouilidis, Christos; Pistofidis, Petros; Fournaris, Apostolos; Bevilacqua, Maurizio; Durazo-Cardenas, Isidro; Botsaris, Pantelis N.; Katsouros, Vassilis; Koulamas, Christos; Starr, Andrew G.
Maintenance management and engineering practice has progressed to adopt approaches which aim to reach maintenance decisions not by means of pre-specified plans and recommendations but increasingly on the basis of best contextually relevant available information and knowledge, all considered against stated objectives. Different methods for automating event detection, diagnostics and prognostics have been proposed, which may achieve very high performance when appropriately adapted and tuned to serve the needs of well defined tasks. However, the scope of such solutions is often narrow and without a mechanism to include human contributed intervention and knowledge contribution. This paper presents a conceptual framework of integrating automated detection and diagnostics and human contributed knowledge in a single architecture. This is instantiated by an e-maintenance platform comprising tools for both lower level information fusion as well as for handling higher level knowledge. Well structured maintenance relationships, such as those present in a typical FMECA study, as well as on the job human contributed compact knowledge are exploited to this end. A case study presenting the actual workflow of the process in an industrial setting is employed to pilot test the approach.
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.
Open Access
Cyber-physical systems for performance monitoring in production intralogistics
(Elsevier, 2020-02-01) Mörth, O.; Emmanouilidis, Christos; Hafner, N.; Schadler, M.
The realization of a higher business value in manufacturing requires optimized internal logistics systems in terms of operational performance, uptime and sustainability. This paper deals with the introduction of Internet of Things (IoT) to unlock new capabilities for enhancing the performane of intralogistics. Specifically, it introduces a design perspective for IoT-driven analytics in intralogistics, within a Cyber-Physical Systems (CPS) approach. Such an approach enables the creation of data process chains linked to performance measurement for intralogistics, a prerequisite for optimisining logistics operations within production environments. An overview of key performance indicators for this domain is offered, followed by an outline of recent research on IoT and CPS and the role of context information management for IoT-enabled data process chains. The conceptual model is illustrated through a representative use case of a CPS demonstrator for performance monitoring in intralogistics. The application implements a simple data process chain, starting from the acquisition and processing of data from a conveyor testbed, followed by the determination and visualization of appropriate performance monitoring information on a dashboard.
Open Access
Data-driven wheel slip diagnostics for improved railway operations
(Elsevier, 2022-09-27) Namoano, Bernadin; Ruiz-Carcel, Cristobal; Emmanouilidis, Christos; Starr, Andrew G.
Wheel slip activity detection is crucial in railway maintenance, as it can contribute to avoiding wheel damage but also track deteriorations leading to significant maintenance costs, trains delays, as well as the risk of accidents. Wheel slip activity is characterised by lower adhesion between track and wheel, especially in braking conditions, locking the wheels. It is complex to model or predict, being influenced by a multitude of factors including ambient conditions, global vehicle load, track and axle quality, leaves and objects present on the rail, steep incline, oxidation of the rails, and braking forces applied to the wheels. This paper presents a combined wavelet and tuned Long-Short Term Memory (LSTM) approach for the detection of wheel slip from time series data collected from real-world trains. Results provide evidence of superior performance over methods such as decision trees and random forests, naïve Bayes, k-nearest neighbours, logistic regression, and support vector machines.
Open Access
A decision-making framework for the design of local production networks under largescale disruptions
(Elsevier, 2021-11-03) Haddad, Yousef; Salonitis, Konstantinos; Emmanouilidis, Christos
In this paper, a model-based decision-making framework for the design of localized networked production systems under largescale disruptions is developed. The framework consists of optimization and agent-based simulation models that run successively in an iterative manner, gradually improving the performance of the perceived system. The framework integrates uncertainty, provides decisions at different decision-making levels and embeds an algorithm that allows for communication between demand nodes and production sites once inventory shortages occur. The framework has been applied on a case study for the design of localized production and distribution networks, powered by additive manufacturing (AM), in South East England during the early stages of the COVID-19 pandemic outbreak. Results revealed that implementing the framework indeed results in performance improvements to AM-powered production networks, particularly with regards to inventory shortages and lead time.
Open Access
A design approach to IoT endpoint security for production machinery monitoring
(MDPI, 2019-05-16) Tedeschi, Stefano; Emmanouilidis, Christos; Mehnen, Jorn; Roy, Rajkumar
The Internet of Things (IoT) has significant potential in upgrading legacy production machinery with monitoring capabilities to unlock new capabilities and bring economic benefits. However, the introduction of IoT at the shop floor layer exposes it to additional security risks with potentially significant adverse operational impact. This article addresses such fundamental new risks at their root by introducing a novel endpoint security-by-design approach. The approach is implemented on a widely applicable production-machinery-monitoring application by introducing real-time adaptation features for IoT device security through subsystem isolation and a dedicated lightweight authentication protocol. This paper establishes a novel viewpoint for the understanding of IoT endpoint security risks and relevant mitigation strategies and opens a new space of risk-averse designs that enable IoT benefits, while shielding operational integrity in industrial environments.
Open Access
Design of emergency response manufacturing networks: a decision-making framework
(Elsevier, 2021-02-10) Haddad, Yousef; Salonitis, Konstantinos; Emmanouilidis, Christos
In times of large-scale crises, seemingly streamlined supply chains could become prone to unforeseen disruptions, leading to interruption in the provision of vital supplies. This could lead to severe consequences if such interruptions include vital products, such as lifesaving medical supplies or healthcare workers’ protective gear. Shortages of vital supplies could occur due to unexpected sharp spike in demand, where manufacturers are unable to produce the necessary quantities required to meet the unusual demand. They could also be the result of a disruption in the product’s supply chain, originating in another country, or even continent, worse affected by the crisis. In either case, localized production, enabled by efforts and resources of local establishments and individuals, could provide a contingency means to produce such vital products to serve local needs, temporarily. Motivated by the growing availability of advanced manufacturing technologies, in particular additive manufacturing (AM), this paper aims to develop a decision-making framework for the design of AM enabled local manufacturing networks in times of crises. The framework consists of complementing interrelated optimization and simulation models that operate iteratively in an uncertain environment, until a local production network, producing the desired performance targets, emerges. Finally, a case study inspired by the shortages of medical supplies, and healthcare workers’ personal protective equipment (PPE), during the worldwide 2020 outbreak of the COVID-19 coronavirus is employed to demonstrate the applicability of the framework
Open Access
Design of redistributed manufacturing networks: a model-based framework
(Cranfield University, 2020-10) Haddad, Yousef; Salonitis, Konstantinos; Emmanouilidis, Christos
Throughout the last century, manufacturing has been characterised by mass production conducted in central facilities, benefitting from economies-of-scale. These central facilities supply, and are supplied by sprawling, complex supply chains that are slow to adapt to demand changes and supply disruptions. As production strategies are gradually shifting from economies-of-scale to economies-of-scope to cater for increasingly complex heterogeneous demand and shorter product life cycles, new configurations are required to enable manufacturing systems to accommodate these demand changes efficiently. One area that has the potential to improve the responsiveness of manufacturing systems is redistributed manufacturing (RdM). RdM is a manufacturing paradigm where production is performed in a network of small, autonomous and geographically distributed facilities. Motivated by the potential opportunities that RdM could bring, this thesis develops a model-based decision-making framework for the design and operation of RdM networks. The framework is context-independent, addresses strategic, tactical and operational decision-making levels and accounts for the interdependence between these decisions in a stochastic environment. The framework is validated methodically through computational experiments on two case studies of different natures and objectives. Experts opinions were solicited throughout the design stage of this research, the implementation of the case studies and the analysis of the results. Results reveal that even when the objectives of the modelled systems are substantially different, the framework generates consistent outputs. The main takeout from the experiments’ results is that the RdM paradigm consistently produces significantly better service level performance, demonstrated by fewer occurrences of unmet demands and shorter lead times. However, although sufficiently close, the RdM paradigm is not as cost efficient as the traditional centralised manufacturing paradigm.
Open Access
Design of redistributed manufacturing systems: a model-based decision-making framework
(Taylor & Francis, 2021-08-06) Haddad, Yousef; Salonitis, Konstantinos; Emmanouilidis, Christos
In this paper, a decision-making framework for the design of redistributed manufacturing (RdM) networks is developed. Redistributed manufacturing, a manufacturing paradigm greatly empowered by the Industry 4.0 toolset, is the shift in production towards geographically dispersed interconnected facilities. The framework is context independent, accounts for the collective impact of all decision-making levels on one another in an iterative manner, and incorporates uncertainty. The framework has been applied to a case study in the aerospace spare parts production sector. Results indicated that the RdM paradigm demonstrated considerable improvements in service level when compared with a traditional centralized counterpart, while it was not as competitive with regards to total cost. This paper contributes to the literature on model-based distributed manufacturing systems design under uncertainty, and enables informed decision-making regarding the redistribution of resources and decentralization of decision-making. The novelty of this paper is the approach employed to handle complexity, nonlinear interrelationships and uncertainty, within the domain of RdM network design. These computationally demanding attributes are handled through simulation, and only their impact is passed back to an analytical model that generates the RdM network.
Open Access
Detecting wheel slip from railway operational data through a combined wavelet, long short-term memory and neural network classification method
(Elsevier, 2024-11) Namoano, Bernadin; Emmanouilidis, Christos; Starr, Andrew
— Detecting wheel slip is important in railway operations, to prevent damage to wheels and tracks, reduce maintenance costs, improve safety and enhance passenger comfort. Slip activity is characterised by reduced adhesion between the wheel and the rail and limits effective braking or acceleration, causing also operational risks. It is influenced by environmental conditions, vehicle load, track and axle quality, contaminants, inclines, rail oxidation, and braking forces. This paper introduces an innovative method for wheel slip detection in operational trains, utilizing wavelet analysis combined with Long-Short Term Memory (LSTM) modelling. This method analyzes operational data to effectively identify wheel slip, showing promising results when compared to traditional classification-based machine learning methods such as decision trees, forests, logistic regression, naïve Bayes, and support vector machines. This novel approach addresses the complexities of wheel slip detection and is capable of identifying the conditions leading to slip several seconds prior to the commencement of the slip event, offering a practical solution for real-world railway systems.
Open Access
Development of a context-aware internet of things framework for remote monitoring services
(Cranfield University, 2020-12) Al-Shdifat, Ali M. A.; Emmanouilidis, Christos; Starr, Andrew
Asset management is concerned with the management practices necessary to maximise the value delivered by physical engineering assets. Internet of Things (IoT)-generated data are increasingly considered as an asset and the data asset value needs to be maximised too. However, asset-generated data in practice are often collected in non-actionable form. Moreover, IoT data create challenges for data management and processing. One way to handle challenges is to introduce context information management, wherein data and service delivery are determined through resolving the context of a service or data request. This research was aimed at developing a context awareness framework and implementing it in an architecture integrating IoT with cloud computing for industrial monitoring services. The overall aim was achieved through a methodological investigation consisting of four phases: establish the research baseline, define experimentation materials and methods, framework design and development, as well as case study validation and expert judgment. The framework comprises three layers: the edge, context information management, and application. Moreover, a maintenance context ontology for the framework has developed focused on modelling failure analysis of mechanical components, so as to drive monitoring services adaptation. The developed context-awareness architecture is expressed business, usage, functional and implementation viewpoints to frame concerns of relevant stakeholders. The developed framework was validated through a case study and expert judgement that provided supporting evidence for its validity and applicability in industrial contexts. The outcomes of the work can be used in other industrially-relevant application scenarios to drive maintenance service adaptation. Context adaptive services can help manufacturing companies in better managing the value of their assets, while ensuring that they continue to function properly over their lifecycle.
Open Access
A digital maintenance practice framework for circular production of automotive parts
(Elsevier, 2020-12-18) Turner, Christopher J.; Okorie, O.; Emmanouilidis, Christos; Oyekan, J.
The adoption of the Circular Economy paradigm by industry leads to increased responsibility of manufacturing to ensure a holistic awareness of the environmental impact of its operations. In mitigating negative effects in the environment, current maintenance practice must be considered, not just for the reduction of its own direct impact but also for its potential contribution to a more sustainable lifecycle for the manufacturing operation, its products and related services. Focusing on the matching of digital technologies to maintenance practice in the automotive sector, this paper outlines a framework for organisations pursuing the integration of environmentally aware solutions in their production systems. This research acts as a primer for digital maintenance practice within the Circular Economy and the utilisation of Industry 4.0 technologies for this purpose
Open Access
Digital technology enablers for resilient and customer driven food value chains
(Springer, 2020-08-25) Emmanouilidis, Christos; Bakalis, Serafim
Food production chains have to respond to disrupted global markets and dynamic customer demands. They are coming under pressure to move from a supply to a demand-driven business model. The inherent difficulties in the lifecycle management of food products, their perishable nature, the volatility in global and regional supplier and customer markets, and the mix of objective and subjective drivers of customer demand and satisfaction, compose a challenging food production landscape. Businesses need to navigate through dynamically evolving operational risks and ensure targeted performance in terms of supply chain resilience and agility, as well as transparency and product assurance. While the industrial transition to digitalised and automated food production chains is seen as a response to such challenges, the contribution of industry 4.0 technology enablers towards this aim is not sufficiently well understood. This paper outlines the key features of high performing food production chains and performs a mapping between them and enabling technologies. As digitalisation initiatives gain priority, such mapping can help with the prioritisation of technology enablers on delivering key aspects of high performing food production chains.
Open Access
Doors datasets
(Cranfield University, 2020-08-18 11:55) Namoano, Bernadin; Starr, Andrew; Emmanouilidis, Christos; Ruiz Carcel, Cristobal
Data represents urban train condition monitoring raw data. Data contains, current and door motions during opening and closing periods.
Open Access
Enabling the human in the loop: Linked data and knowledge in industrial cyber-physical systems
(Elsevier, 2019-03-19) Emmanouilidis, Christos; Pistofidis, Petros; Bertoncelj, Luka; Katsouros, Vassilis; Fournaris, Apostolos; Koulamas, Christos; Ruiz-Carcel, Cristobal
Industrial Cyber-Physical Systems have benefitted substantially from the introduction of a range of technology enablers. These include web-based and semantic computing, ubiquitous sensing, internet of things (IoT) with multi-connectivity, advanced computing architectures and digital platforms, coupled with edge or cloud side data management and analytics, and have contributed to shaping up enhanced or new data value chains in manufacturing. While parts of such data flows are increasingly automated, there is now a greater demand for more effectively integrating, rather than eliminating, human cognitive capabilities in the loop of production related processes. Human integration in Cyber-Physical environments can already be digitally supported in various ways. However, incorporating human skills and tangible knowledge requires approaches and technological solutions that facilitate the engagement of personnel within technical systems in ways that take advantage or amplify their cognitive capabilities to achieve more effective sociotechnical systems. After analysing related research, this paper introduces a novel viewpoint for enabling human in the loop engagement linked to cognitive capabilities and highlighting the role of context information management in industrial systems. Furthermore, it presents examples of technology enablers for placing the human in the loop at selected application cases relevant to production environments. Such placement benefits from the joint management of linked maintenance data and knowledge, expands the power of machine learning for asset awareness with embedded event detection, and facilitates IoT-driven analytics for product lifecycle management.
Open Access
Engines datasets
(Cranfield University, 2020-08-18 11:56) Namoano, Bernadin; Starr, Andrew; Emmanouilidis, Christos; Ruiz Carcel, Cristobal
Data represents urban DMU trains engines condition monitoring data.
Open Access
Framework to assess the maturity level of learning analytics in higher education and drive learning services improvement
(Cranfield University, 2020-05) Alenezi, Abdullah; Emmanouilidis, Christos; Al-Ashaab, Ahmed
This research was aimed at developing a framework that could be utilised to assess the maturity level of learning analytics (LA) in virtual learning environment (VLE) in higher education institutions (HEI). The assessment of the maturity level of LA in VLE in HEI contributes to enhancing the educational learning programmes and academic services offering to the learners. The successful implementation of LA in an HEI could help improve teaching and learning processes, thereby improving students’ learning experiences (Larrabee Sønderlund et al., 2019; Sclater et al., 2016; Waheed et al., 2020). However, most HEIs often do not know where to start from in implementing programmes for using VLE and LA; thus, the contribution of this study to offer guidance for HEIs. In order to develop the LA maturity assessment framework, a multi-phases methodological approach was adopted which involved 6 key phases (understanding the literature, a field study to gain a high-level perspective of VLE and LA, development of LA maturity model, development of a performance measurement tool, formulation of road map recommendations, and case study validation and expert judgment). The developed LA maturity model comprises of five levels: basic (level 1), developing (level 2), functional (level 3), advanced (level 4) and optimised (level 5). In determining these LA maturity level, the performance measurement tool has to be applied. This performance measurement tool assesses an HEI’s performance in four key components of LA: process, infrastructure, data and human resources and skills. The LA maturity model and performance measurement tool facilitate the road map recommendations. Based on an HEI’s assessed LA maturity level, recommendations are suggested on how progress can be made in LA implementation. The developed LA assessment framework was validated through case studies (PAAET and Cranfield University) and expert judgement that proved its validity and application to different educational contexts. The case study validation showed the differences in performance scores and maturity levels of the two HEIs with specific recommendations relevant to each context being made. Expert judgements highlighted the contribution of the framework to LA which is a relatively new area of research.

Browsing by Author "Emmanouilidis, Christos"

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