---
_id: '2014'
abstract:
- lang: eng
  text: Industrial applications are in transition towards modular and flexible architectures
    that are capable of self-configuration and -optimisation. This is due to the demand
    of mass customisation and the increasing complexity of industrial systems. The
    conversion to modular systems is related to challenges in all disciplines. Consequently,
    diverse tasks such as information processing, extensive networking, or system
    monitoring using sensor and information fusion systems need to be reconsidered.
    The focus of this contribution is on distributed sensor and information fusion
    systems for system monitoring, which must reflect the increasing flexibility of
    fusion systems. This contribution thus proposes an approach, which relies on a
    network of self-descriptive intelligent sensor nodes, for the automatic design
    and update of sensor and information fusion systems. This article encompasses
    the fusion system configuration and adaptation as well as communication aspects.
    Manual interaction with the flexibly changing system is reduced to a minimum.
article_number: '601'
author:
- first_name: Alexander
  full_name: Fritze, Alexander
  last_name: Fritze
- first_name: Uwe
  full_name: Mönks, Uwe
  id: '1825'
  last_name: Mönks
- first_name: Christoph-Alexander
  full_name: Holst, Christoph-Alexander
  id: '64782'
  last_name: Holst
- first_name: Volker
  full_name: Lohweg, Volker
  id: '1804'
  last_name: Lohweg
  orcid: 0000-0002-3325-7887
citation:
  ama: Fritze A, Mönks U, Holst C-A, Lohweg V. An Approach to Automated Fusion System
    Design and Adaptation. <i>Sensors</i>. 2017;17(3). doi:<a href="https://doi.org/
    https://doi.org/10.3390/s17030601"> https://doi.org/10.3390/s17030601</a>
  apa: Fritze, A., Mönks, U., Holst, C.-A., &#38; Lohweg, V. (2017). An Approach to
    Automated Fusion System Design and Adaptation. <i>Sensors</i>, <i>17</i>(3). <a
    href="https://doi.org/ https://doi.org/10.3390/s17030601">https://doi.org/ https://doi.org/10.3390/s17030601</a>
  bjps: <b>Fritze A <i>et al.</i></b> (2017) An Approach to Automated Fusion System
    Design and Adaptation. <i>Sensors</i> <b>17</b>.
  chicago: Fritze, Alexander, Uwe Mönks, Christoph-Alexander Holst, and Volker Lohweg.
    “An Approach to Automated Fusion System Design and Adaptation.” <i>Sensors</i>
    17, no. 3 (2017). <a href="https://doi.org/ https://doi.org/10.3390/s17030601">https://doi.org/
    https://doi.org/10.3390/s17030601</a>.
  chicago-de: Fritze, Alexander, Uwe Mönks, Christoph-Alexander Holst und Volker Lohweg.
    2017. An Approach to Automated Fusion System Design and Adaptation. <i>Sensors</i>
    17, Nr. 3. doi:<a href="https://doi.org/ https://doi.org/10.3390/s17030601,">
    https://doi.org/10.3390/s17030601,</a> .
  din1505-2-1: '<span style="font-variant:small-caps;">Fritze, Alexander</span> ;
    <span style="font-variant:small-caps;">Mönks, Uwe</span> ; <span style="font-variant:small-caps;">Holst,
    Christoph-Alexander</span> ; <span style="font-variant:small-caps;">Lohweg, Volker</span>:
    An Approach to Automated Fusion System Design and Adaptation. In: <i>Sensors</i>
    Bd. 17 (2017), Nr. 3'
  havard: A. Fritze, U. Mönks, C.-A. Holst, V. Lohweg, An Approach to Automated Fusion
    System Design and Adaptation, Sensors. 17 (2017).
  ieee: A. Fritze, U. Mönks, C.-A. Holst, and V. Lohweg, “An Approach to Automated
    Fusion System Design and Adaptation,” <i>Sensors</i>, vol. 17, no. 3, 2017.
  mla: Fritze, Alexander, et al. “An Approach to Automated Fusion System Design and
    Adaptation.” <i>Sensors</i>, vol. 17, no. 3, 601, 2017, doi:<a href="https://doi.org/
    https://doi.org/10.3390/s17030601"> https://doi.org/10.3390/s17030601</a>.
  short: A. Fritze, U. Mönks, C.-A. Holst, V. Lohweg, Sensors 17 (2017).
  ufg: '<b>Fritze, Alexander et. al. (2017)</b>: An Approach to Automated Fusion System
    Design and Adaptation, in: <i>Sensors</i> <i>17</i> (<i>3</i>).'
  van: Fritze A, Mönks U, Holst C-A, Lohweg V. An Approach to Automated Fusion System
    Design and Adaptation. Sensors. 2017;17(3).
date_created: 2019-11-25T08:52:14Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
doi: ' https://doi.org/10.3390/s17030601'
intvolume: '        17'
issue: '3'
keyword:
- information fusion
- intelligent sensor
- knowledge-based system
- self-configuration
- sensor fusion
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.mdpi.com/1424-8220/17/3/601/htm
oa: '1'
publication: Sensors
publication_identifier:
  issn:
  - 1424-8220
publication_status: published
status: public
title: An Approach to Automated Fusion System Design and Adaptation
type: journal_article
user_id: '15514'
volume: 17
year: 2017
...
---
_id: '2044'
abstract:
- lang: eng
  text: Sensors, and also actuators or external sources such as databases, serve as
    data sources in order to realise condition monitoring of industrial applications
    or the acquisition of characteristic parameters like production speed or reject
    rate. Modern facilities create such a large amount of complex data that a machine
    operator is unable to comprehend and process the information contained in the
    data. Thus, information fusion mechanisms gain increasing importance. Besides
    the management of large amounts of data, further challenges towards the fusion
    algorithms arise from epistemic uncertainties (incomplete knowledge) in the input
    signals as well as conflicts between them. These aspects must be considered during
    information processing to obtain reliable results, which are in accordance with
    the real world. The analysis of the scientific state of the art shows that current
    solutions fulfil said requirements at most only partly. This article proposes
    the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing
    observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving)
    fusion algorithm to reduce the impact of conflicts on the fusion result. The performance
    of the contribution is shown by its evaluation in the scope of a machine condition
    monitoring application under laboratory conditions. Here, the MACRO system yields
    the best results compared to state-of-the-art fusion mechanisms. The utilised
    data is published and freely accessible.
article_number: '1798'
author:
- first_name: Uwe
  full_name: Mönks, Uwe
  id: '1825'
  last_name: Mönks
- first_name: Helene
  full_name: Dörksen, Helene
  id: '46416'
  last_name: Dörksen
- first_name: Volker
  full_name: Lohweg, Volker
  id: '1804'
  last_name: Lohweg
  orcid: 0000-0002-3325-7887
- first_name: Michael
  full_name: Hübner, Michael
  last_name: Hübner
citation:
  ama: Mönks U, Dörksen H, Lohweg V, Hübner M. Information Fusion of Conflicting Input
    Data. <i>Sensors</i>. 2016. doi:<a href="https://doi.org/10.3390/s16111798">10.3390/s16111798</a>
  apa: Mönks, U., Dörksen, H., Lohweg, V., &#38; Hübner, M. (2016). Information Fusion
    of Conflicting Input Data. <i>Sensors</i>. <a href="https://doi.org/10.3390/s16111798">https://doi.org/10.3390/s16111798</a>
  bjps: <b>Mönks U <i>et al.</i></b> (2016) Information Fusion of Conflicting Input
    Data. <i>Sensors</i>.
  chicago: Mönks, Uwe, Helene Dörksen, Volker Lohweg, and Michael Hübner. “Information
    Fusion of Conflicting Input Data.” <i>Sensors</i>, 2016. <a href="https://doi.org/10.3390/s16111798">https://doi.org/10.3390/s16111798</a>.
  chicago-de: Mönks, Uwe, Helene Dörksen, Volker Lohweg und Michael Hübner. 2016.
    Information Fusion of Conflicting Input Data. <i>Sensors</i>. doi:<a href="https://doi.org/10.3390/s16111798,">10.3390/s16111798,</a>
    .
  din1505-2-1: '<span style="font-variant:small-caps;">Mönks, Uwe</span> ; <span style="font-variant:small-caps;">Dörksen,
    Helene</span> ; <span style="font-variant:small-caps;">Lohweg, Volker</span> ;
    <span style="font-variant:small-caps;">Hübner, Michael</span>: Information Fusion
    of Conflicting Input Data. In: <i>Sensors</i> (2016)'
  havard: U. Mönks, H. Dörksen, V. Lohweg, M. Hübner, Information Fusion of Conflicting
    Input Data, Sensors. (2016).
  ieee: U. Mönks, H. Dörksen, V. Lohweg, and M. Hübner, “Information Fusion of Conflicting
    Input Data,” <i>Sensors</i>, 2016.
  mla: Mönks, Uwe, et al. “Information Fusion of Conflicting Input Data.” <i>Sensors</i>,
    1798, 2016, doi:<a href="https://doi.org/10.3390/s16111798">10.3390/s16111798</a>.
  short: U. Mönks, H. Dörksen, V. Lohweg, M. Hübner, Sensors (2016).
  ufg: '<b>Mönks, Uwe et. al. (2016)</b>: Information Fusion of Conflicting Input
    Data, in: <i>Sensors</i>.'
  van: Mönks U, Dörksen H, Lohweg V, Hübner M. Information Fusion of Conflicting Input
    Data. Sensors. 2016;
date_created: 2019-11-26T15:03:36Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
doi: 10.3390/s16111798
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.mdpi.com/1424-8220/16/11/1798
oa: '1'
publication: Sensors
publication_identifier:
  issn:
  - 1424-8220
publication_status: published
status: public
title: Information Fusion of Conflicting Input Data
type: journal_article
user_id: '68554'
year: 2016
...