Articles | Volume 10, issue 2
https://doi.org/10.5194/jsss-10-233-2021
https://doi.org/10.5194/jsss-10-233-2021
Regular research article
 | 
24 Aug 2021
Regular research article |  | 24 Aug 2021

Influence of synchronization within a sensor network on machine learning results

Tanja Dorst, Yannick Robin, Sascha Eichstädt, Andreas Schütze, and Tizian Schneider

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Latest update: 20 May 2024
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Short summary
Synchronization problems within distributed sensor networks are a major challenge in the field of Industry 4.0. In this paper, artificially generated time shifts between sensor data and their influence on remaining useful lifetime prediction of electromechanical cylinders are investigated. It is shown that time shifts within sensor data lead to poor remaining useful lifetime predictions. However, this prediction can be significantly improved using various methods as shown in this contribution.