Mobile Automated Diagnostics of Stress State and Residual Life Prediction for a Component under Intensive Random Dynamic Loads

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Mozgova, I.; Yanchevskyi, I.; Gerasymenko, M.; Lachmayer, R.: Mobile Automated Diagnostics of Stress State and Residual Life Prediction for a Component under Intensive Random Dynamic Loads. In: Procedia Manufacturing 24 (2018), S. 210-215. DOI: https://doi.org/10.1016/j.promfg.2018.06.037

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/3812

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Sum total of downloads: 93




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Abstract: 
This article presents an approach developed for collecting and processing data about the actual stress state of a structural component of a technical product and to estimate its residual fatigue life in case random dynamic loadings. As input data sensor values are being used from which the operating loads acting on a component are calculated. For the realization of the experiments a suitable mobile measuring device has been developed. The device is designed to digitize sensor signals at certain positions identified by simulation results, to collect this data in an internal memory, to mathematically analyze this data in real time, to process this data in parallel for the purpose of identifying characteristic information, to restore the stress state of the component and to estimate the residual fatigue life. The created concept provides an opportunity to realize the "intellectualization" of structural components without radical changes in their construction and allows supplementing components with additional functions for collecting and analyzing data during the usage phase of the components life cycle, what is an integral part of an Industry 4.0 product.
License of this version: CC BY-NC-ND 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2018
Appears in Collections:Fakultät für Maschinenbau

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 64 68.82%
2 image of flag of United States United States 11 11.83%
3 image of flag of Namibia Namibia 4 4.30%
4 image of flag of China China 4 4.30%
5 image of flag of United Kingdom United Kingdom 3 3.23%
6 image of flag of No geo information available No geo information available 1 1.08%
7 image of flag of Ukraine Ukraine 1 1.08%
8 image of flag of Mexico Mexico 1 1.08%
9 image of flag of Greece Greece 1 1.08%
10 image of flag of Estonia Estonia 1 1.08%
    other countries 2 2.15%

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