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 (Sensors, 2024)
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 (<semantics>Ttz1</semantics>) thermostat and supply air temperature (<semantics>Tsa</semantics>) and chilled water supply air temperature (<semantics>Tchws</semantics>) 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 <semantics>Ttz1</semantics> thermostat and <semantics>Tsa</semantics> 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 <semantics>Ttz1</semantics> thermostat had low noise, a 7-day data volume, or sufficient variables and when the <semantics>Tsa</semantics> and <semantics>Tchws</semantics> sensors had low noise, a 14-day data volume, or limited variables.