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      Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

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          Abstract

          One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.

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          Most cited references 74

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          Rolling element bearing diagnostics—A tutorial

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            The spectral kurtosis: a useful tool for characterising non-stationary signals

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              A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                29 March 2021
                April 2021
                : 21
                : 7
                Affiliations
                Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, 917 24 Trnava, Slovakia; lukas.spendla@ 123456stuba.sk (L.S.); michal.kebisek@ 123456stuba.sk (M.K.); rastislav.duris@ 123456stuba.sk (R.D.); maximilian.stremy@ 123456stuba.sk (M.S.)
                Author notes
                [* ]Correspondence: pavol.tanuska@ 123456stuba.sk ; Tel.: +421-918-646-061
                Article
                sensors-21-02376
                10.3390/s21072376
                8037397
                33805557
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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