Browsing by Author "Latsou, Christina"

Now showing 1 - 11 of 11
  • Thumbnail Image
    ItemOpen Access
    Design for Digitally Enabled Industrial Product-Services Systems
    (Elsevier, 2023-07-08) Erkoyuncu, John Ahmet; Farsi, Maryam; Addepalli, Sri; Latsou, Christina
    Planning the life cycle of industrial product-service systems (IPS2) is highly challenging due to uncertainties experienced in predicting supply (e.g. spares) and demand (e.g. availability) related factors. Whilst digitalisation offers numerous exciting avenues, industry is finding it challenging to realise the potential benefits. This paper focuses on how to design the set of digital technologies and methodologies that serve as enabling capabilities to optimise value across the life cycle. This involves offering a step by step process to compare alternative improvement opportunities (e.g. data modelling, digital twins) with the justification to support investment decisions. The systematic design methodology is tested on an aerospace component, demonstrating the added value of digitally enabled IPS2.
  • Thumbnail Image
    ItemOpen Access
    A design framework for technology prioritisation in the context of through-life engineering services
    (Elsevier, 2021-06-02) Chi, Jie; Latsou, Christina; Erkoyuncu, John Ahmet; Grenyer, Alex; Rushton, Keith R.; Brocklebank, Simon
    Lack of methods on standardising the prioritisation of technologies within the context of Through-life Engineering Services (TES) has been identified. Inspired by the TES value streams and support activity assets, existing within a common TES framework, a new design framework for technologies prioritisation is proposed. A dynamic toolkit to identify the most suitable technology is also developed, using a Quality Function Deployment method, Analytic Hierarchy Process and ROI analysis. A real case study from the defence sector is employed to validate the developed design framework and toolkit; the results show a well-structured guide that can effectively simplify the decision-making process.
  • Thumbnail Image
    ItemOpen Access
    Developing an ontological framework for effective data quality assessment and knowledge modelling
    (Cranfield University, 08/11/2022) Latsou, Christina; Garcia I Minguell, Marta; Sonmez, Ayse Nur; Orteu I Irurre, Roger; Palmisano, Martin Mark; Landon-Valdez, Suresh; Erkoyuncu, John Ahmet; Addepalli, Pavan; Sibson, Jim; Silvey, Olly
    Big data has become a major challenge in the 21st century, with research being carried out to classify, mine and extract knowledge from data obtained from disparate sources. Abundant data sources with non-standard structures complicate even more the arduous process of data integration. Currently, the major requirement is to understand the data available and detect data quality issues, with research being conducted to establish data quality assessment methods. Further, the focus is to improve data quality and maturity so that early onset of problems can be predicted and handled effectively. However, the literature highlights that comprehensive analysis, and research of data quality standards and assessment methods are still lacking. To handle these challenges, this paper presents a structured framework to standardise the process of assessing the quality of data and modelling the knowledge obtained from such an assessment by implementing an ontology. The main steps of the framework are: (i) identify user’s requirements; (ii) measure the quality of data considering data quality issues, dimensions and their metrics, and visualise this information into a data quality assessment (DQA) report; and (iii) capture the knowledge from the DQA report using an ontology that models the DQA insights in a standard reusable way. Following the proposed framework, an Excel-based tool to measure the quality of data and identify emerging issues is developed. An ontology, created in Protégé, provides a standard structure to model the data quality insights obtained from the assessment, while it is frequently updated to enrich captured knowledge, reducing time and costs for future projects. An industrial case study in the context of Through life Engineering Services, using operational data of high value engineering assets, is employed to validate the proposed ontological framework and tool; the results show a well-structured guide that can effectively assess data quality and model knowledge.
  • Thumbnail Image
    ItemOpen Access
    Digital twin architecture for a sustainable control system in aircraft engines
    (Springer , 2024-08-08) Farsi, Maryam; Namoano, Bernadin; Latsou, Christina; Subhadu, Vaishnav Venkata; Deng, Haoxuan; Sun, Zhen; Zheng, Bohao; D’Amico, Davide; Erkoyuncu, John Ahmet; Karakoc, T. Hikmet; Colpan, Can Ozgur; Dalkiran, Alper
    Over the past decades, climate change has remained one of the major global challenges in the world. In the aviation and aerospace industry, the environmental sustainable development strategies towards carbon-neutral mainly focus on efficiency and demand measures, sustainable fuels, renewable energies, and removal and carbon offsetting. The carbon dioxide equivalent (CO2e) emissions footprint of an aircraft is primarily determined by energy and fuel efficiency. The advanced engine control systems of an aircraft can optimise the engine performance to achieve energy efficiency, fuel optimal consumption, and emission reduction. This paper proposed a digital twin architecture of a sustainable aircraft control system that allows the system to collect, analyse, and optimise sustainability-related data and to provide insight to operators, engineers, maintainers, and designers. The required information, knowledge and insight databases across flight environment, engine specification, and gas emissions are identified. The research argued that the proposed architecture could enhance engine energy efficiency, fuel consumption, and CO2e footprint reduction and enable (near) real-time data monitoring, proactive anomaly detection, forecasting, and intelligent decision-making within an automated sustainability control system. This research suggests ontology-based digital twin as an effective approach to further develop a cognitive twin that facilitates automated decision-making within the aircraft control system.
  • Thumbnail Image
    ItemOpen Access
    Digital twin integration in multi-agent cyber physical manufacturing systems
    (Elsevier, 2021-11-09) Latsou, Christina; Farsi, Maryam; Erkoyuncu, John Ahmet; Morris, Geoffrey
    Complex manufacturing and supply chain systems consist of concurrent labour-intensive processes and procedures with repetitive time-consuming tasks and multiple quality checks. These features may pose challenges for the efficient operation and management, while manual tasks may significantly increase human errors or near misses, having impact on the propagation of effects and parallel interactions within these systems. In order to handle the aforementioned challenges, a digital twin (DT) integrated in a multi-agent cyber-physical manufacturing system (CPMS) with the help of RFID technology is proposed. The proposed reference architecture tends to improve the trackability and traceability of complex manufacturing processes. In this research work, the interactions occurring both within a single complex manufacturing system and between multiple sites within a supply chain are considered. For the implementation of the integrated DT-CPMS, a simulation model employing the agent-based modelling technique is developed. A case study from a cryogenic supply chain in the UK is also selected to show the application and validity of the proposed digital solution. The results prove that the DT-CPMS architecture can improve system’s performance in terms of human, equipment and space utilisations.
  • Thumbnail Image
    ItemOpen Access
    Digital twin-enabled automated anomaly detection and bottleneck identification in complex manufacturing systems using a multi-agent approach
    (Elsevier, 2023-02-11) Latsou, Christina; Farsi, Maryam; Erkoyuncu, John Ahmet
    Digital twin (DT) models are increasingly being used to improve the performance of complex manufacturing systems. In this context, DTs automatically enabling anomaly detection, such as increase in orders, and bottleneck identification, such as shortage of products, can significantly enhance decision-making to mitigate the consequences of the identified bottlenecks. The existing literature has mainly focused on implementing top-down approaches for analysing the bottlenecks without considering the emergent behaviour of micro-level agents, including inventory levels and human resources, and their impact on the macro-level system’s performance. In order to handle the aforementioned challenges, this paper extends the current literature by proposing a novel DT integrated in a multi-agent cyber physical system (CPS) for detecting anomalies in sensor data, while identifying and removing bottlenecks that emerge during the operation of complex manufacturing systems. An extended 5 C CPS architecture, using multi-agent approach, is implemented to allow DT integration. The agent-based simulation technique enables capturing the probabilistic variability, and aggregate parallelism and dynamism of parallel dynamic interactions within the DT-CPS. A new single agent at the exo-level of the multi-level agent-based modelling structure, called the ‘monitoring agent’, is introduced in this research. The agent detects anomalies and identify bottlenecks through communicating with other agents in different levels automatically. The DT-CPS provides feedback automatically to the physical space to remove and mitigate the identified bottlenecks. The proposed DT based multi-agent CPS has been tested successfully on a real case study in a cryogenic warehouse shop-floor from the cell and gene therapy industry. The performance of the studied cryogenic warehouse is continuously measured using real-time sensor data. The analyses of the results show that the proposed DT-CPS improves the utilisation rates of human resources, on average, by 30% supporting decision making and control in complex manufacturing systems.
  • Thumbnail Image
    ItemOpen Access
    Influencing operational policing strategy by predictive service analytics
    (Association for Information Systems, 2018-01-31) Jackson, Lisa; Stoneman, Melanie-Jane; Callaghan, Heather; Zhang, Hanjing; Latsou, Christina; Dunnett, Sarah; Mao, Lei
    Everyday there are growing pressures to ensure that services are delivered efficiently, with high levels of quality and with acceptability of regulatory standards. For the Police Force, their service requirement is to the public, with the police officer presence being the most visible product of this criminal justice provision. Using historical data from over 10 years of operation, this research demonstrates the benefits of using data mining methods for knowledge discovery in regards to the crime and incident related elements which impact on the Police Force service provision. In the UK, a Force operates over a designated region (macro-level), which is further subdivided into Beats (micro-level). This research also demonstrates differences between the outputs of micro-level and macro-level analytics, where the lower level analysis enables adaptation of the operational Policing strategy. The evidence base provided through the analysis supports decisions regarding further investigations into the capability of flexible neighbourhood policing practices; alongside wider operations i.e. optimal officer training times.
  • Thumbnail Image
    ItemOpen Access
    Multi-channel anomaly detection using graphical models
    (Springer, 2024-12-31) Namoano, Bernadin; Latsou, Christina; Erkoyuncu, John Ahmet
    Anomaly detection in multivariate time-series data is critical for monitoring asset conditions, enabling prompt fault detection and diagnosis to mitigate damage, reduce downtime and enhance safety. Existing literature predominately emphasises temporal dependencies in single-channel data, often overlooking interrelations between features in multivariate time-series data and across multiple channels. This paper introduces G-BOCPD, a novel graphical model-based annotation method designed to automatically detect anomalies in multi-channel multivariate time-series data. To address internal and external dependencies, G-BOCPD proposes a hybridisation of the graphical lasso and expectation maximisation algorithms. This approach detects anomalies in multi-channel multivariate time-series by identifying segments with diverse behaviours and patterns, which are then annotated to highlight variations. The method alternates between estimating the concentration matrix, which represents dependencies between variables, using the graphical lasso algorithm, and annotating segments through a minimal path clustering method for a comprehensive understanding of variations. To demonstrate its effectiveness, G-BOCPD is applied to multichannel time-series obtained from: (i) Diesel Multiple Unit train engines exhibiting faulty behaviours; and (ii) a group of train doors at various degradation stages. Empirical evidence highlights G-BOCPD's superior performance compared to previous approaches in terms of precision, recall and F1-score.
  • Thumbnail Image
    ItemOpen Access
    A multi-objective approach for resilience-based system design optimisation of complex manufacturing systems
    (Elsevier, 2021-06-02) Latsou, Christina; Erkoyuncu, John Ahmet; Farsi, Maryam
    Disruptive events in complex manufacturing systems (CMS), characterised by labour-intensive processes and repetitive activities, render these systems vulnerable. In order to tackle this challenge, an approach for resilience-based system design optimisation is proposed. The approach: (i) introduces a dynamic multi-dimensional resilience metric; and (ii) formulates the resilience as a multi-objective optimisation problem to improve CMSs resilience by finding an optimal human resource allocation model, considering design factors including redundancy, resources capacity and roles. The case study, selected to test the validity of the presented approach, show improvement in resilience and efficiency, in terms of throughput, resources utilisation and restoration time.
  • Thumbnail Image
    ItemOpen Access
    RFID application in a multi-agent cyber physical manufacturing system
    (MDPI, 2020-10-29) Farsi, Maryam; Latsou, Christina; Erkoyuncu, John Ahmet; Morris, Geoffrey
    In manufacturing supply chains with labour-intensive operations and processes, individuals perform various types of manual tasks and quality checks. These operations and processes embrace engagement with various forms of paperwork, regulation obligations and external agreements between multiple stakeholders. Such manual activities can increase human error and near misses, which may ultimately lead to a lack of productivity and performance. In this paper, a multi-agent cyber-physical system (CPS) architecture with radio frequency identification (RFID) technology is presented to assist inter-layer interactions between different manufacturing phases on the shop floor and external interactions with other stakeholders within a supply chain. A dynamic simulation model in the AnyLogic software is developed to implement the CPS-RFID solution by using the agent-based technique. A case study from cryogenic warehousing in cell and gene therapy has been chosen to test the validity of the presented CPS-RFID architecture. The analyses of the simulation results show improvement in efficiency and productivity, in terms of resource time-in-system
  • Thumbnail Image
    ItemOpen 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.