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  • 1.
    Fornlöf, Veronica
    et al.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. GKN.
    Sandberg, Ulf
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Syberfeldt, Anna
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Almgren, Torgny
    GKN Aerospace Engine Systems.
    More reliable aircraft engine maintenance optimization by a classification framework for on-condition parts2014Inngår i: Proceedings of the 6th Swedish Production Symposium, SPS14, Gothenburg, Sweden, Chalmers , 2014Konferansepaper (Fagfellevurdert)
  • 2.
    Sandberg, Ulf
    Dept of Engineering Science, University West, Trollhättan, Sweden.
    How an enlarged maintenance function affects the performance of industrial maintenance and maintenance services2013Inngår i: International Journal of Strategic Engineering Asset Management (IJSEAM), ISSN 1759-9733, E-ISSN 1759-9741, Vol. 1, nr 3, s. 265-275Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Today, many companies struggle with maintenance work established by pre–requisites during installation, start–up and use of plants, systems and machines. In search for improvements in existing maintenance organisations, it is easy to conclude that they look like and is a direct consequence of previous engineering decisions and activities. Maintenance work, however, only exists in the operation phase with weak coupling to what created it. Using an empirical knowledge base, several examples of this narrow view on maintenance is given along with the consequences it results in today. By linking together activities in the establishment life–cycle phase with the operation phase it is shown how an enlarged maintenance function could improve the situation. Using references to the leading actors creating the frontier today it is illustrated where most companies could be tomorrow. The paper hopefully will contribute to how maintenance processes and activities are being designed, set–up and carried out.

  • 3.
    Sandberg, Ulf
    et al.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Schmidt, Bernard
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Wang, Lihui
    Kungliga Tekniska Högskolan.
    Management of factory and maintenance information for multiple production and product life-cycle phases2014Inngår i: / [ed] B.K.N.Rao, 2014Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Maintenance is crucial for future manufacturing systems. An extended local knowledge is essential to increase precision and efficiency, but also for improvements of the maintained object itself. Approaches exist that closes the loop from end-user to vendor, but intra loops are not so well developed.

    This article discusses ways to interconnect and manage data and knowledge flow between work processes in user and vendor life-cycles. It aims to inspire improvements in existing approaches, closer connections between producer and customer, between users, and improved quality of maintenance work via factory-, company-, or group-wide data and knowledge about similar types of equipment.

  • 4.
    Schmidt, Bernard
    et al.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Sandberg, Ulf
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Wang, Lihui
    Department of Production Engineering Royal Institute of Technology, Sweden.
    Next Generation Condition Based Predictive Maintenance2014Inngår i: Proceedings of The 6th International Swedish Production Symposium 2014 / [ed] Johan Stahre, Björn Johansson, Mats Björkman, 2014Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and make decisions upon this prediction. The main aim of the presented research is to achieve an improvement in condition based Predictive Maintenance through the Cloud-based approach with usage of the largest information content possible. The objective of this paper is to outline the first steps of a framework to handle and process maintenance, production and factory related data from the first life-cycle phase to the operation and maintenance phase.

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