Tracking Long-Term Drift in Wireless Sensor Networks Using Long-Term Memory and Data Fusion

Theresa Loss, Oliver Gerler, Alexander Bergmann

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

Wireless sensor networks are used to guarantee optimal and safe operation of difficult-To-reach industrial and civil structures. Due to their exposed mounting location, the sensors experience severe environmental influences. This leads to erosion and ageing of components which result in drifting standard values. Therefore, online tracking of standard values is paramount to guarantee optimal performance. An algorithm has been developed by fusing measurement data across several sensors during their steady-state. The system is able to track drifting standard values by using long-Term memory. Simulations show that the algorithm successfully differentiates between measured data and drift of standard values. Simulations have been verified by applying the algorithm to real-world data of several months. Results show that the algorithm is able to track the drift of standard values, thereby maintaining full sensitivity.

Originalspracheenglisch
Titel2018 International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
ISBN (elektronisch)9781538644232
DOIs
PublikationsstatusVeröffentlicht - 6 Nov 2018
Veranstaltung13th International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018 - Pilsen, Tschechische Republik
Dauer: 4 Sep 20187 Sep 2018

Konferenz

Konferenz13th International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018
LandTschechische Republik
OrtPilsen
Zeitraum4/09/187/09/18

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Data fusion
Wireless sensor networks
Data storage equipment
Sensors
Mountings
Erosion
Aging of materials

Schlagwörter

    ASJC Scopus subject areas

    • !!Electrical and Electronic Engineering
    • !!Safety, Risk, Reliability and Quality

    Dies zitieren

    Loss, T., Gerler, O., & Bergmann, A. (2018). Tracking Long-Term Drift in Wireless Sensor Networks Using Long-Term Memory and Data Fusion. in 2018 International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018 [8526133] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DIAGNOSTIKA.2018.8526133

    Tracking Long-Term Drift in Wireless Sensor Networks Using Long-Term Memory and Data Fusion. / Loss, Theresa; Gerler, Oliver; Bergmann, Alexander.

    2018 International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018. Institute of Electrical and Electronics Engineers, 2018. 8526133.

    Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

    Loss, T, Gerler, O & Bergmann, A 2018, Tracking Long-Term Drift in Wireless Sensor Networks Using Long-Term Memory and Data Fusion. in 2018 International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018., 8526133, Institute of Electrical and Electronics Engineers, Pilsen, Tschechische Republik, 4/09/18. https://doi.org/10.1109/DIAGNOSTIKA.2018.8526133
    Loss T, Gerler O, Bergmann A. Tracking Long-Term Drift in Wireless Sensor Networks Using Long-Term Memory and Data Fusion. in 2018 International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018. Institute of Electrical and Electronics Engineers. 2018. 8526133 https://doi.org/10.1109/DIAGNOSTIKA.2018.8526133
    Loss, Theresa ; Gerler, Oliver ; Bergmann, Alexander. / Tracking Long-Term Drift in Wireless Sensor Networks Using Long-Term Memory and Data Fusion. 2018 International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018. Institute of Electrical and Electronics Engineers, 2018.
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