Status information from avionics systems is typically transmitted to airlines, but aircraft cabin systems remain largely disconnected and frequently reliant on manual, paper-based logbooks for defect recording. This r...
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Status information from avionics systems is typically transmitted to airlines, but aircraft cabin systems remain largely disconnected and frequently reliant on manual, paper-based logbooks for defect recording. This results in error-prone processes that compromise data consistency and complicate maintenance planning. Digitalisation offers solutions to these challenges by enabling predictive maintenance, real-time monitoring and streamlined data sharing, improving operational reliability and efficiency. However, developing such systems is inherently complex due to operational constraints and stringent safety and security regulations. Model-based systems Engineering (MBSE) effectively manages complexity, provide standardised system visualisation and enhance multidisciplinary communication. From a methodological perspective, approaches from literature suitable for addressing aviation maintenance systems were selected and enhanced with allocation techniques and subsequently applied to create system models for both current and digitalised aircraft cabins. This paper showcases MBSE's relevance to develop digitalised aircraft cabin systems by using the systems Modeling language (SysML) enabling stakeholders to visualise system architectures and to make better-informed design decisions. The analysis of the presented SysML models highlights the error-prone structure of current non-digitalised aircraft cabin systems while illustrating new use cases unlocked by digitalisation. A model-based comparison underscores the improved efficiency, reliability and predictive capabilities achieved through digital transformation. This study demonstrates that MBSE provides qualitative advantages in system development by enhancing stakeholder collaboration, clarifying complex system architectures, and providing actionable insights into system behaviour and improvements.
Digital servitization (DS) enabled by the Industrial Internet of Things (IIoT) is essential for securing long-term competitiveness in manufacturing. The literature identifies the need for developing data models for mu...
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Digital servitization (DS) enabled by the Industrial Internet of Things (IIoT) is essential for securing long-term competitiveness in manufacturing. The literature identifies the need for developing data models for multichannel communications across IIoT devices that fulfil the DS vision. This is crucial for avoiding isolated systems based on proprietary solutions and for promoting data sharing and interoperability across existing and future DS applications. Accordingly, this study proposes a data model for multichannel communication that facilitates IIoT-enabled DS for smart production logistics (SPL). We present three findings from a case study focussing on material handling in a manufacturing company. First, this study provides a model with four modelling profiles, including IIoT devices, databases, and services for multichannel communication. Second, it shows how the proposed modelling profiles transfer information across monitoring, control, optimisation, and autonomous decision services. Third, it presents the operational benefits of applying the proposed data models, including improvements in the delivery, makespan, and energy of material handling. These findings are essential for capturing, processing, and transferring information across products, services, and software databases in IIoT-enabled DS for SPL. They are relevant for manufacturing managers and academics and improve our understanding of IIoT-enabled DS's deployment for SPL in manufacturing.
This paper advocates for Digital Twin (DT) technology as a pivotal solution to address the complexities of Complex Operations Environments (COEs). Recognizing the need for a thorough understanding of COEs and their DT...
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ISBN:
(纸本)9783031744815;9783031744822
This paper advocates for Digital Twin (DT) technology as a pivotal solution to address the complexities of Complex Operations Environments (COEs). Recognizing the need for a thorough understanding of COEs and their DTs, a methodology is introduced to bridge existing gaps. Given the lack of a universal definition, the approach leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Latent Dirichlet Allocation (LDA) to extract insights, facilitating the development of a comprehensive definition for COE and DT. The methodology integrates Ontology and systems modelling language (SysML) to provide a semantic and conceptual model of COE and DT. Ontology enriches the semantic understanding, exploring existence and entity relationships, while SysML ensures clear and concise communication through standardized graphical representation. This paper aims to present a methodology to achieve a precise understanding of COEs and their corresponding DTs, providing a robust foundation for addressing operational complexities in dynamic environments.
The High-Voltage (HV) network within an Electric Vehicle (EV) will typically comprise different energy sources such as fuel cells, batteries and ultracapacitors integrated together through the use of both unidirection...
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The High-Voltage (HV) network within an Electric Vehicle (EV) will typically comprise different energy sources such as fuel cells, batteries and ultracapacitors integrated together through the use of both unidirectional and bidirectional DC-DC converters. Given the multitude of feasible HV network designs, there are obvious advantages in having a unifying control architecture that facilitates the Energy Management (EM) control task. Within this paper, a control Reference Architecture (RA) is proposed that can be employed as a template for the design of the EM control function. Example EM control systems are presented each derived from the same RA, but relating to a different physical configuration of HV network. Simulation results are presented to verify the functional performance of the control systems. In each case, the design trade-offs associated with the functional performance of the EM strategy and the non-functional requirements of modularity and reusability are discussed.
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