Cognitive digital twin: An approach to improve the maintenance management

D’Amico, Rosario Davide; Erkoyuncu, John Ahmet; Addepalli, Sri; Penver, Steve

doi:10.1016/j.cirpj.2022.06.004

Cited by 53 publications

(9 citation statements)

References 48 publications

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“…This approach, based on DT technology, allows for the prediction of maintenance risks and effectively reduces the chances of equipment failure. By improving maintenance efficiency, it is clear that predictive maintenance is the future trend for mechanical process system maintenance [167][168][169]. To optimize equipment failure monitoring, prediction, and maintenance decisions, He et al [170] proposed a complex equipment health management approach.…”

Section: Fault Diagnosis and Predictive Maintenancementioning

confidence: 99%

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Hu,

Li,

et al. 2024

Intelligent and Sustainable Manufacturing

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Digital twin technology develops virtual models of objects digitally, simulating their real-world behavior based on data. It aims to reduce product development cycles and costs through feedback between the virtual and real worlds, data fusion analysis, and iterative decision-making optimization. Traditional manufacturing processes often face challenges such as poor real-time monitoring and interaction during machining, difficulties in diagnosing equipment failures, and significant errors in machining. Digital twin technology offers a powerful solution to these issues. Initially, a comprehensive review of the research literature was conducted to assess the current research scope and trends. This was followed by an explanation of the basic concepts of digital twins and the technical pathway for integrating digital twins into intelligent manufacturing including outlining the essential technologies for creating a system of interaction between the virtual and real worlds, enabling multimodel fusion, data sensing, algorithm-based prediction, and intelligent decision-making. Moreover, the application of digital twins in intelligent manufacturing throughout the product life cycle was detailed, covering product design, manufacturing, and service stages. Specifically, in the manufacturing phase, a model based on heat conduction theory and visualization was used to construct a time-varying error model for the motion axis, leading to experiments predicting the time-varying error in the hole spacing of a workpiece. These experiments achieved a minimum prediction error of only 0.2 μm compared to the actual error. By compensating for time-varying errors in real time, the variability in the hole spacing error decreased by 69.19%. This paper concludes by summarizing the current state of digital twins in intelligent manufacturing and projecting future trends in key technologies, application areas, and data use, providing a basis for further research.

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Section: Fault Diagnosis and Predictive Maintenancementioning

confidence: 99%

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Hu,

Li,

et al. 2024

Intelligent and Sustainable Manufacturing

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“…Furthermore, a conceptual architecture is proposed, mapping the tools materializing cognition within the DT core, along with a cognitive process that enables resilience in production through the utilization of CDTs. Papers [26][27][28][29][30][31][32][33][34][35][36][37][38][39] are included in this analysis to illustrate that the CDT concept is utilized in various industrial domains, such as process industry [28], manufacturing [29][30][31], maintenance management [32], construction [33][34][35][36][37] where works [36,37] propose ML models suitable for the design of DTs in the construction industry, and health care [38,39]. Additionally, publication [40] with its thematic focus on DTs in learning and education attracted the attention of the authors of this paper.…”

Section: Related Workmentioning

confidence: 99%

Towards a Domain-Neutral Platform for Sustainable Digital Twin Development

Savić,

Segedinac,

Konjović

et al. 2023

Sustainability

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In this paper, we propose an abstract domain-neutral architecture for a cognitive digital twin (CDT) and a software platform to develop such CDTs, including machine reasoning capabilities. Sustainable development refers here to an abstract model that enables a holistic view of limiting resources and has an ability to adapt to different application domains while reusing existing resources. The proposed solution allows for a unified abstract representation and the development of a wide range of diverse digital twins, as well as facilitating their interoperability. The abstract architecture consists of a four-layer structure (observation/actuation layer, data management layer, reasoning layer, and simulation layer) with an upper ontology to which the domain ontology of the specific CDT is mapped. The architecture relies on semantic web technologies, including ontology-based reasoning using OWL, and a loosely coupled, component-based service-oriented software architecture. The platform utilizes a microservice architecture that enables separate, loosely coupled services on each layer, message queues to provide asynchronous communication, and possesses cloud technologies to achieve scalability. The proposed approach was validated by implementing a software platform prototype and demonstrating its key features through two dissimilar scenarios. The first scenario demonstrates simple sustainable energy management through IoT systems inside smart buildings, while the second one demonstrates knowledge quality management based on knowledge space theory.

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“…Such a task is a key factor especially in the development of the Digital Twin of a product [28] and when dealing with the design of such an entity. In fact, the literature has been heavily invested in the conception of the Digital Twin as an asset for the monitoring and maintenance phases [29][30][31]. As such, the Digital Twin can be seen as a post-design tool.…”

mentioning

confidence: 99%

An Orchestration Method for Integrated Multi-Disciplinary Simulation in Digital Twin Applications

Brusa,

Dagna,

Delprete

et al. 2023

Aerospace

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In recent years, the methodology of Model-Based System Engineering (MBSE) has become relevant to the design of complex products, especially when safety critical systems need to be addressed. It allows, in fact, the deployment of product development directly through some digital models, allowing an effective traceability of requirements, being allocated upon the system functions, components, and parts. This approach enhances the designer capabilities in controlling the product development, manufacturing and after-market services. However, the application of such a methodology requires overcoming several technological barriers, especially in terms of models integration. The interoperability and management of several models—developed within different software to cover multiple levels of detail across several technical disciplines—is still very difficult, despite the level of maturation achieved by Systems Engineering. This paper describes a possible approach to provide such a connection between tools to allow a complete multi-disciplinary and heterogeneous simulation to analyse complex systems, such as safety-critical ones, which are typical of aerospace applications. Such an application is within a defined industrial context, placing particular attention on the compatibility of the approach with the legacy processes and tools.

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Cognitive digital twin: An approach to improve the maintenance management

Cited by 53 publications

References 48 publications

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Towards a Domain-Neutral Platform for Sustainable Digital Twin Development

An Orchestration Method for Integrated Multi-Disciplinary Simulation in Digital Twin Applications

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