Modelling Fluid Loss Faults in an Industrial Pressure Sensor

Kamran Soltani, Oleg Bushuev, Ekaterina Tugova, Mina Ghanbari, Manus Patrick Henry, Ghader Rezazadeh

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

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Abstract

The efficient operation of industrial processes requires the timely and accurate diagnosis of faults in process equipment, particularly sensors, as acting on faulty measurement data can result in inefficient or dangerous operation. A common fault mode in industrial pressure sensors is mechanical damage resulting in the leakage of the internal oil (used to transmit external pressure to the sensing element) and the development of an air pocket within the device. In previous work, we have experimentally determined the faulty measurement characteristics of a commercial pressure sensor, where the sensor manufacturer has provided modified sensors with calibrated degrees of oil loss. The current paper develops a mathematical model of this tenso-resistive pressure sensor, which describes and explains the impact that oil loss, and hence the presence of an air pocket, has on the static measurement response.

Original languageEnglish
Title of host publicationProceedings - 2020 Global Smart Industry Conference, GloSIC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages215-222
Number of pages8
ISBN (Electronic)9781728180755
ISBN (Print)9781728180762
DOIs
Publication statusPublished - 17 Nov 2020
Event2020 Global Smart Industry Conference, GloSIC 2020 - Chelyabinsk, Russian Federation
Duration: 17 Nov 202019 Nov 2020

Publication series

NameProceedings - 2020 Global Smart Industry Conference, GloSIC 2020

Conference

Conference2020 Global Smart Industry Conference, GloSIC 2020
CountryRussian Federation
CityChelyabinsk
Period17/11/2019/11/20

Bibliographical note

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Keywords

  • Galerkin approach
  • oil-loss leakage
  • pressure sensors
  • sensor faults modelling

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Information Systems and Management
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Safety, Risk, Reliability and Quality

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