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

Theresa Loss, Oliver Gerler, Alexander Bergmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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.

Original languageEnglish
Title of host publication2018 International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781538644232
DOIs
Publication statusPublished - 6 Nov 2018
Event13th International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018 - Pilsen, Czech Republic
Duration: 4 Sep 20187 Sep 2018

Conference

Conference13th International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018
CountryCzech Republic
CityPilsen
Period4/09/187/09/18

Fingerprint

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

Keywords

  • data fusion
  • drift
  • harsh environments
  • sensor networks
  • tracking

ASJC Scopus subject areas

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

Cite this

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.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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, 13th International Conference on Diagnostics in Electrical Engineering, Diagnostika 2018, Pilsen, Czech Republic, 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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