Integrating maintenance within the production process through a flexible E-maintenance platform

Fumagalli, Luca; Macchi, Marco

doi:10.1016/j.ifacol.2015.06.292

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…In particular, the presented representation could be one of the enablers to the recently-formulated Synchro-push approach to production management, as described in [83]. Besides, it will be relevant to develop advanced maintenance systems featuring Prognostics and Health Management capabilities, combined with the modularization of supported functions and information to provide high flexibility for different applications in manufacturing [84,85]. Further research to better frame the role of manufacturing and logistics ontologies in production environments based on the Synchro-push paradigm is envisioned, in particular to understand the benefits brought by such an approach through its application to simulated and real industrial cases.…”

Section: Discussionmentioning

confidence: 99%

Modelling internal logistics systems through ontologies

Negri

Perotti

Fumagalli

et al. 2017

Computers in Industry

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Industry is facing an era characterised by unpredictable market changes and by a turbulent competitive environment. The key to compete in such a context is to achieve high degrees of responsiveness by means of high flexibility and rapid reconfiguration capabilities. The deployment of modular solutions seems to be part of the answer to face these challenges. Semantic modelling and ontologies may represent the needed knowledge representation to support flexibility and modularity of production systems, when designing a new system or when reconfiguring an existing one. Although numerous ontologies for production systems have been developed in the past years, they mainly focus on discrete manufacturing, while logistics aspects, such as those related to internal logistics and warehousing, have not received the same attention. The paper aims at offering a representation of logistics aspects, reflecting what has become a de-facto standard terminology in industry and among researchers in the field. Such representation is to be used as an extension to the already-existing production systems ontologies that are more focused on manufacturing processes. The paper presents the structure of the hierarchical relations within the examined internal logistics elements, namely Storage and Transporters, structuring them in a series of classes and sub-classes, suggesting also the relationships and the attributes to be considered to complete the modelling. Finally, the paper proposes an industrial example with a miniload system to show how such a modelling of internal logistics elements could be instanced in the real world.

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Section: Discussionmentioning

confidence: 99%

Modelling internal logistics systems through ontologies

Negri

Perotti

Fumagalli

et al. 2017

Computers in Industry

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“…Visualisation of spare parts and maintenance instructions using AR. Fumagalli and Macchi (2015) E-maintenance platform utilising web services approach and statistical techniques.…”

Section: Methods For Improvedmentioning

confidence: 99%

Intelligent decision support for maintenance: an overview and future trends

Turner

Emmanouilidis

Tomiyama

et al. 2019

International Journal of Computer Integrated Manufacturing

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The changing nature of manufacturing, in recent years, is evident in industry's willingness to adopt network connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision making systems. While e-maintenance practice provides a framework for internet connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of a comprehensive framework for its processing, analysis and use should be a valuable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data and the provision of comprehensive framework for its processing analysis and use, allowing future systems to enable 'Human in the loop' interactions.

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“…Despite the plethora of CPM frameworks existing both in the academic and the industrial realms, there is still a large gap for effective implementation of CPM extensively in industry, mainly due to complexity of these solutions and their life cycle [4]. E-maintenance refers to the convergence of emerging information and communication technologies with systems which take into account the resources, services and management to enable decision making in a proactive way [3] and thus, to enhance and extend the CPM framework [4]. E-maintenance incorporates emerging technologies capable of optimizing maintenance-related workflows and integrating business performance with other components of e-enterprise [4].…”

Section: Condition-based Predictive Maintenance and E-maintenancementioning

confidence: 99%

“…Maintenance is a key operation function, since it is related to all the manufacturing processes and focuses not only on avoiding the equipment breakdown but also on improving business performance in terms of productivity, quality and logistics management [2,3]. Existing maintenance strategies can broadly be distinguished to three categories: (a) Breakdown Maintenance; (b) Time-based Preventive Maintenance; (c) Condition-based Predictive Maintenance (CPM) [2].…”

Section: Introductionmentioning

confidence: 99%

Condition-Based Predictive Maintenance in the Frame of Industry 4.0

Bousdekis

Mentzas

2017

IFIP Advances in Information and Communication Technology

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The emergence of Industry 4.0 leads to the optimization of all the industrial operations management. Maintenance is a key operation function, since it contributes significantly to the business performance. However, the definition and conceptualization of Condition-based Predictive Maintenance (CPM) in the frame of Industry 4.0 is not clear yet. In the current paper, we: (i) explicitly define CPM in the frame of Industry 4.0 (alternatively referred as Proactive Maintenance); (ii) develop a unified approach for its implementation; and, (iii) provide a conceptual architecture for associated information systems.

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Integrating maintenance within the production process through a flexible E-maintenance platform

Cited by 7 publications

References 17 publications

Modelling internal logistics systems through ontologies

Modelling internal logistics systems through ontologies

Intelligent decision support for maintenance: an overview and future trends

Condition-Based Predictive Maintenance in the Frame of Industry 4.0

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