Articles | Volume 10, issue 2
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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Cited articles

Beyer, K., Goldstein, J., Ramakrishnan, R., and Shaft, U.: When Is “Nearest Neighbor” Meaningful?, in: Database Theory – ICDT'99, Springer, Berlin, Heidelberg, 217–235, 1999. a
Dorst, T.: Sensor data set of 3 electromechanical cylinder at ZeMA testbed (2 kHz), Zenodo [data set],, 2019. a, b
Dorst, T., Ludwig, B., Eichstädt, S., Schneider, T., and Schütze, A.: Metrology for the factory of the future: towards a case study in condition monitoring, in: 2019 IEEE International Instrumentation and Measurement Technology Conference, Auckland, New Zealand, 439–443,, 2019. a
Dorst, T., Eichstädt, S., Schneider, T., and Schütze, A.: Propagation of uncertainty for an Adaptive Linear Approximation algorithm, in: SMSI 2020 – Sensor and Measurement Science International, 366–367,, 2020. a
Dorst, T., Robin, Y., Schneider, T., and Schütze, A.: Automated ML Toolbox for Cyclic Sensor Data, in: MSMM 2021 – Mathematical and Statistical Methods for Metrology, 2021a. a, b
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.