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  • 1. Byttner, S.
    et al.
    Rögnvaldsson, Thorsteinn
    Örebro University, School of Science and Technology.
    Svensson, Magnus
    Volvo, Gothenburg, Sweden.
    Consensus self-organized models for fault detection (COSMO)2011In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 24, no 5, p. 833-839Article in journal (Refereed)
    Abstract [en]

    Methods for equipment monitoring are traditionally constructed from specific sensors and/or knowledge collected prior to implementation on the equipment. A different approach is presented here that builds up knowledge over time by exploratory search among the signals available on the internal field bus system and comparing the observed signal relationships among a group of equipment that perform similar tasks. The approach is developed for the purpose of increasing vehicle uptime, and is therefore demonstrated in the case of a city bus and a heavy duty truck. However, it also works fine for smaller mechatronic systems like computer hard-drives. The approach builds on an onboard self-organized search for models that capture relations among signal values on the vehicles' data buses, combined with a limited bandwidth telematics gateway and an off-line server application where the parameters of the self-organized models are compared. The presented approach represents a new look at error detection in commercial mechatronic systems, where the normal behavior of a system is actually found under real operating conditions, rather than the behavior observed in a number of laboratory tests or test-drives prior to production of the system. The approach has potential to be the basis for a self-discovering system for general purpose fault detection and diagnostics. (c) 2011 Elsevier Ltd. All rights reserved.

  • 2.
    Dragone, Mauro
    et al.
    Trinity College Dublin, Dublin, Ireland.
    Amato, Giuseppe
    ISTI-CNR, Pisa, Italy.
    Bacciu, Davide
    Università di Pisa, Pisa, Italy.
    Chessa, Stefano
    Università di Pisa, Pisa, Italy.
    Coleman, Sonya
    University of Ulster, Derry, United Kingdom.
    Di Rocco, Maurizio
    Örebro University, School of Science and Technology.
    Gallicchio, Claudio
    Università di Pisa, Pisa, Italy.
    Gennaro, Claudio
    ISTI-CNR, Pisa, Italy.
    Lozano, Hector
    Tecnalia, Bilbao, Spain.
    Maguire, Liam
    University of Ulster, Derry, United Kingdom.
    McGinnity, Martin
    University of Ulster, Derry, United Kingdom.
    Micheli, Alessio
    Università di Pisa, Pisa, Italy.
    O'Hare, Gregory M. P.
    University College Dublin, Dublin, Ireland.
    Renteria, Arantxa
    Tecnalia, Bilbao, Spain.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Vairo, Claudio
    ISTI-CNR, Pisa, Italy.
    Vance, Philip
    University of Ulster, Derry, United Kingdom.
    A cognitive robotic ecology approach to self-configuring and evolving AAL systems2015In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 45, p. 269-280Article in journal (Refereed)
    Abstract [en]

    Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits.

  • 3.
    Palm, Rainer
    Örebro University, Department of Technology.
    Multiple-step-ahead prediction in control systems with Gaussian process models and TS-fuzzy models2007In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 20, no 8, p. 1023-1035Article in journal (Refereed)
    Abstract [en]

    In this paper one-step-ahead and multiple-step-ahead predictions of time series in disturbed open loop and closed loop systems using Gaussian process models and TS-fuzzy models are described. Gaussian process models are based on the Bayesian framework where the conditional distribution of output measurements is used for the prediction of the system outputs. For one-step-ahead prediction a local process model with a small past horizon is built online with the help of Gaussian processes. Multiple-step-ahead prediction requires the knowledge of previous outputs and control values as well as the future control values. A "naive" multiple-step-ahead prediction is a successive one-step-ahead prediction where the outputs in each consecutive step are used as inputs for the next step of prediction. A global TS-fuzzy model is built to generate the nominal future control trajectory for multiple-step-ahead prediction. In the presence of model uncertainties a correction of the so computed control trajectory is needed. This is done by an internal feedback between the two process models. The method is tested on disturbed time invariant and time variant systems for different past horizons. The combination of the TS-fuzzy model and the Gaussian process model together with a correction of the control trajectory shows a good performance of the multiple-step-ahead prediction for systems with uncertainties. © 2007 Elsevier Ltd. All rights reserved.

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