Information Fusion of Conflicting Input Data

Abstract

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.

A Novel Classification Technique of Landsat-8 OLI Image-Based Data Visualization: The Application of Andrews’ Plots and Fuzzy Evidential Reasoning

“…Mönks et al focused on the fuzzified balanced two-layer conflict solving (µBalYLCS) algorithm, which originates from DS theory and operates on fuzzy sets. The algorithm works well in situations with high data conflict [18]. Furthermore, Lu et al [19] have confirmed the effectiveness and usefulness of fuzzy logic combined with an extension of DS theory in data fusion.…”

“…, 100) be an array of 100 discrete Andrews' curve values and i be indices from 2 to 99 (since G(1) and G(100) cannot be the candidates). G[i] can be defined as a PP if it satisfies Equation (17); otherwise, it can be defined as an NP by Equation (18).…”

“…The data points are arranged in descending order from bands 6 to 2 (lower to higher frequencies). Each Andrews' curve is divided into 100 discrete values and its TAD is obtained by the iterative procedure in Equations (17) and (18).…”

An Approach to Automated Fusion System Design and Adaptation

“…Its basic concept is summarised in the following. The entire theoretical background is elaborated in [6,11]. MACRO’s structure is depicted in Figure 1.…”

“…For more information on the human group decision-making background of MACRO, the reader is referred to [6,12]. It was shown that the application of this approach for condition monitoring purposes is beneficial compared to state-of-the-art approaches [6,11,13]. …”