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  • 1.
    Fornlöf, Veronica
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. GKN Aerospace Engine Systems, Trollhättan, Sweden.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Almgren, Torgny
    GKN Aerospace Engine Systems, Trollhättan, Sweden.
    RUL estimation and maintenance optimization for aircraft engines: A system of system approach2016In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, no 4, 450-461 p.Article in journal (Refereed)
    Abstract [en]

    An aircraft engine is a system of systems with several degrees of complexity. It is important to perform the correct amount of maintenance at each individual maintenance event. A mathematical replacement model is used to ensure that the correct maintenance is performed. The reliability of the results from the mathematical replacement model will be improved if there is a better way to estimate the life length for on-condition engine parts.

  • 2.
    Gerdes, Mike
    et al.
    Aero - Aircraft Design and Systems Group, Hamburg University of Applied Sciences, Hamburg, Germany.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Division of Operation and Maintenance Engineering, Luleå University of Technology, Luleå, Sweden.
    Fuzzy condition monitoring of recirculation fans and filters2016In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, no 4, 469-479 p.Article in journal (Refereed)
    Abstract [en]

    A reliable condition monitoring is needed to be able to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies however only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that outputs similarity values for decision trees. Experiments confirmed that the method works and showed good classification results. Different fuzzy functions were evaluated to show how the method can be adapted to different problems. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. The goal is to have the probabilities of a sample belonging to each class. Performed experiments showed that the concept is reliable and it also works with decision tree forests (which is shown during this paper) to increase the classification accuracy. Overall the presented concept has the same classification accuracy than a normal decision tree but it offers the user more information about how certain the classification is.

  • 3.
    Sachdeva, Nitin
    et al.
    Department of Operational Research, University of Delhi, Faculty of Mathematical Sciences, Delhi, India.
    Singh, Ompal
    Department of Operational Research, University of Delhi, Faculty of Mathematical Sciences, Delhi, India.
    Kapur, P. K.
    Amity University, Noida, India.
    Galar, Diego
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Multi-criteria intuitionistic fuzzy group decision analysis with TOPSIS method for selecting appropriate cloud solution to manage big data projects2016In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, no 3, 316-324 p.Article in journal (Refereed)
    Abstract [en]

    Today technology that learns from data to forecast future behavior of individuals, organizations, government and country as a whole, is playing a crucial role in the advancement of human race. In fact, the strategic advantage most of the companies today strive for are use of new available technologies like cloud computing and big data. However, today's dynamic business environment poses severe challenges in front of companies as to how to make use of the power of big data with the technical flexibility that cloud computing provides? Therefore, evaluating, ranking and selecting the most appropriate cloud solution to manage big data project is a complex concern which required multi criteria decision environment. In this paper we propose a hybrid TOPSIS method combined with intuitionistic fuzzy set to select appropriate cloud solution to manage big data projects in group decision making environment. In order to collate individual opinions of decision makers for rating the importance of various criteria and alternatives, we employed intuitionistic fuzzy weighted averaging operator. Lastly sensitivity analysis is performed so as to evaluate the impact of criteria weights on final ranking of alternatives.

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