Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments.

Guannan Li, Chongchong Wang, Lamei Liu, Xi Fang, Wei Kuang, Chenglong Xiong

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Article in Sensors (Basel, Switzerland), 2024. 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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6 authors.

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

Chongchong WangSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.

Lamei LiuSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.

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

Wei KuangSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.

Chenglong XiongSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.

Funding

"The 14th Five Year Plan" Hubei Provincial advantaged characteristic disciplines (groups) project of Wuhan University of Science and Technology 2023D0504the National Natural Science Foundation of China 51906181the open Foundation of Anhui Province Key Laboratory of Intelligent Building and Building En-ergy-saving IBES2022KF11The Opening Fund of Key Laboratory of Low-grade Energy Utilization Technologies and Systems of Ministry of Education of China (Chongqing University) LLEUTS-202305the Opening Fund of State Key Laboratory of Green Building in Western China LSKF202316

6 · The paper itself

Abstract

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

Indexed as

data incrementsfault-tolerant controlheating, ventilation, and air-conditioning (HVAC)multiple linear regression–Bayesian inferencesensors

Identifiers

PMID38400309

DOI10.3390/s24041150

PMCPMC10891601

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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.