Model Based Compensation of Thermal Drifts for Multi Electrode Capacitive Sensing

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

Abstract

Capacitive sensing has become a favourable measurement technology for industrial applications. A main advantage of capacitive sensors is the contactless working principle, which enables capacitive sensors for the application in industrial processes with harsh environmental conditions. Multi electrode capacitive sensing is used to obtain information about objects in a region of interest. This requires dedicated model based signal processing techniques and accurate physical models of the measurement process. Industrial processes often entail high temperatures, which leads to thermal drifts of the material values and to thermal expansions within the sensor front end. In order to draw reliable conclusions about the quantities of interest, these effects have to be taken into account. In this paper we present a model based temperature compensation approach for capacitive multi electrode structures, which considers temperature related permittivity changes as well as structural displacements due to thermal expansions to reduce the impact of thermal drifts. The proposed approach is validated on the example of an electrical capacitance tomography sensor.

Original languageEnglish
Title of host publicationI2MTC 2020 - International Instrumentation and Measurement Technology Conference, Proceedings
PublisherIEEE Xplore
ISBN (Electronic)9781728144603
DOIs
Publication statusPublished - 30 Jun 2020
Event2020 IEEE International Instrumentation and Measurement Technology Conference: IEEE I2MTC 2020 - Virtuell, Dubrovnik, Croatia
Duration: 25 May 202028 May 2020
https://i2mtc2020.ieee-ims.org/

Conference

Conference2020 IEEE International Instrumentation and Measurement Technology Conference
Abbreviated titleIEEE I2MTC 2020
Country/TerritoryCroatia
CityVirtuell, Dubrovnik
Period25/05/2028/05/20
Internet address

Keywords

  • Capacitive sensing
  • Harsh environments
  • Model based signal processing

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Instrumentation

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