Machine conditioning by importance controlled information fusion

Mönks, Uwe; Lohweg, Volker

doi:10.1109/etfa.2013.6647984

Cited by 14 publications

(12 citation statements)

References 30 publications

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“…The information generated in the various groups is then combined on organisational level (system layer) to make a global decision. For more information on the human group decision-making background of MACRO the reader is referred to [19]. …”

Section: Multilayer Attribute-based Conflict-reducing Observationmentioning

confidence: 99%

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Information Fusion of Conflicting Input Data

Mönks

Dörksen

Lohweg

et al. 2016

Sensors

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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.

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Section: Multilayer Attribute-based Conflict-reducing Observationmentioning

confidence: 99%

“…The theoretical background and applications of MACRO and μ BalTLCS have been researched over the past years and published in several papers [17,18,19,20,21,22,23,24]. This article summarises the research to present a comprehensive view on MACRO and μ BalTLCS.…”

Section: Introductionmentioning

confidence: 99%

Information Fusion of Conflicting Input Data

Mönks

Dörksen

Lohweg

et al. 2016

Sensors

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“…The information generated in the various groups is then combined on an organisational level (system layer) to make a global decision. For more information on the human group decision-making background of MACRO, the reader is referred to Mönks and Lohweg (2013). It was shown that the application of this approach for hazardous material store monitoring is beneficial compared to state-of-the-art installations (Ehlenbröker et al, 2014).…”

Section: Multilayer Attribute-based Conflict-reducing Observation (Mamentioning

confidence: 99%

“…Detailed information regarding MACRO and µ BalTLCS is found in Mönks et al (2012); Mönks and Lohweg (2013), while optimisations concerning an efficient implementation are found in Mönks and Lohweg (2014). This contribution concentrates on MACRO's fusion on the attribute layer.…”

Section: Multilayer Attribute-based Conflict-reducing Observation (Mamentioning

confidence: 99%

“…Redundancies, which occur by combining at least two information sources to one attribute, are exploited for both (i) detecting sensor faults and (ii) cross-checking the consistency of sensor values. The latter is carried out implicitly by the psychologically inspired fuzzified balanced two-layer conflict solving (µBalTLCS) fusion approach (Lohweg and Mönks, 2010a;Mönks et al, 2012;Mönks and Lohweg, 2013). It creates one output N µ a : [0, 1] n → [0, 1] per attribute a from its inputs.…”

Section: Multilayer Attribute-based Conflict-reducing Observation (Mamentioning

confidence: 99%

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Sensor defect detection in multisensor information fusion

Ehlenbröker¹,

Mönks²,

Lohweg³

2016

J. Sens. Sens. Syst.

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Abstract. In industrial processes a vast variety of different sensors is increasingly used to measure and control processes, machines, and logistics. One way to handle the resulting large amount of data created by hundreds or even thousands of different sensors in an application is to employ information fusion systems. Information fusion systems, e.g. for condition monitoring, combine different sources of information, like sensors, to generate the state of a complex system. The result of such an information fusion process is regarded as a health indicator of a complex system. Therefore, information fusion approaches are applied to, e.g., automatically inform one about a reduction in production quality, or detect possibly dangerous situations. Considering the importance of sensors in the previously described information fusion systems and in industrial processes in general, a defective sensor has several negative consequences. It may lead to machine failure, e.g. when wear and tear of a machine is not detected sufficiently in advance. In this contribution we present a method to detect faulty sensors by computing the consistency between sensor values. The proposed sensor defect detection algorithm exemplarily utilises the structure of a multilayered group-based sensor fusion algorithm. Defect detection results of the proposed method for different test cases and the method's capability to detect a number of typical sensor defects are shown.

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