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A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information

Information Fusion of Conflicting Input Data

inIT—Institute Industrial IT, Ostwestfalen-Lippe University of Applied Sciences, Lemgo 32657, Germany
ESIT—Embedded Systems for Information Technology, Ruhr-University Bochum, Bochum 44801, Germany
Author to whom correspondence should be addressed.
Academic Editors: Xue-Bo Jin, Feng-Bao Yang, Shuli Sun and Hong Wei
Sensors 2016, 16(11), 1798;
Received: 30 July 2016 / Revised: 30 September 2016 / Accepted: 19 October 2016 / Published: 29 October 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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. View Full-Text
Keywords: information fusion; sensor fusion; conflict; evidence theory; Dempster-Shafer theory; possibility theory; fuzzy set theory information fusion; sensor fusion; conflict; evidence theory; Dempster-Shafer theory; possibility theory; fuzzy set theory
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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.55227
    Description: Printing Unit Condition Monitoring: Sensor Data Set
MDPI and ACS Style

Mönks, U.; Dörksen, H.; Lohweg, V.; Hübner, M. Information Fusion of Conflicting Input Data. Sensors 2016, 16, 1798.

AMA Style

Mönks U, Dörksen H, Lohweg V, Hübner M. Information Fusion of Conflicting Input Data. Sensors. 2016; 16(11):1798.

Chicago/Turabian Style

Mönks, Uwe, Helene Dörksen, Volker Lohweg, and Michael Hübner. 2016. "Information Fusion of Conflicting Input Data" Sensors 16, no. 11: 1798.

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