Temperature Uncertainty Reduction Algorithm Based on Temperature Distribution Prior for Optical Sensors in Oil Tank Ground Settlement Monitoring

Ground settlement (GS) in an oil tank determines its structural integrity and commercial service. However, GS monitoring faces challenges, particularly due to the significant temperature differences induced by solar radiation around the tank in daytime. To address this problem, this paper digs out a...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Sensors (Basel, Switzerland) Ročník 24; číslo 7; s. 2341
Hlavní autori: Liu, Tao, Jiang, Tao, Liu, Gang, Sun, Changsen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 01.04.2024
Predmet:
Algorithms
Atmospheric pressure
Ethical aspects
ground settlement monitoring
Liu, Timothy
Normal distribution
Numerical analysis
numerical simulation
optical fiber sensor
Radiation
Sensors
Simulation methods
Systems stability
temperature distribution
temperature uncertainty
temperature uncertainty
ground settlement monitoring
temperature distribution
optical fiber sensor
numerical simulation
ISSN:1424-8220, 1424-8220
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Ground settlement (GS) in an oil tank determines its structural integrity and commercial service. However, GS monitoring faces challenges, particularly due to the significant temperature differences induced by solar radiation around the tank in daytime. To address this problem, this paper digs out a prior and proposes a temperature uncertainty reduction algorithm based on that. This prior has a spatial Gaussian distribution of temperature around the tank, and numerical simulation and practical tests are conducted to demonstrate it. In addition, combining uniformly packaged sensor probes and the spatial prior of temperature, the temperature uncertainty is verified to be Gaussian-distributed too. Then, the overall temperature uncertainty can be captured by Gaussian fitting and then removed. The practical test verified a 91% reduction rate in temperature uncertainty, and this approach enables GS sensors to effectively perform daytime monitoring by mitigating temperature-related uncertainties.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s24072341