Dynamic Calibration Method of Sensor Drift Fault in HVAC System Based on Bayesian Inference.

Guannan Li, Haonan Hu, Jiajia Gao, Xi Fang

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In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

4 authors.

Guannan LiSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.

Haonan HuSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.

Jiajia GaoSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.

Xi FangCollege of Civil Engineering, Hunan University, Changsha 410082, China.

Funding

National Natural Science Foundation of China 51906181the 2021 year Construction Technology Plan Project of Hubei Province, and the Excel-lent Young and Middle-aged Talent in Universities of Hubei Province Q20181110

6 · The paper itself

Abstract

Sensor drift fault calibration is essential to maintain the operation of heating, ventilation and air conditioning systems (HVAC) in buildings. Bayesian inference (BI) is becoming more and more popular as a commonly used sensor fault calibration method. However, this method focused mainly on sensor bias fault, and it could be difficult to calibrate drift fault that changes with time. Therefore, a dynamic calibration method for sensor drift fault of HVAC systems based on BI is developed. Taking the drift fault calibration of the chilled water supply temperature sensor of the chiller as an example, the performance of the proposed dynamic calibration method is evaluated. Results show that the combination of the Exponentially Weighted Moving-Average (EWMA) method with high detection accuracy and the proposed BI dynamic calibration method can effectively improve the calibration accuracy of drift fault, and the Mean Absolute Percentage Error (MAPE) value between the calibrated and normal data is less than 5%.

Indexed as

Bayesian inference (BI)fault detectionfield dynamic calibrationHVAC systemsensor drift fault

Identifiers

PMID35891028

DOI10.3390/s22145348

PMCPMC9319236

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.