Fault detection, diagnosis and calibration of heating, ventilation and air conditioning sensors by combining principal component analysis and improved bayesian inference

Abstract

Sensors are crucial components in heating, ventilation and air conditioning (HVAC) systems. When sensors fault, the HVAC system deviates from normal operation, resulting in an increase in energy consumption and a decline in equipment lifespan. Therefore, it is vital to detect, diagnose and calibrate the sensor faults. Bayesian Inference (BI) in data-driven methods is highly effective for sensor calibration. However, its application faces two major shortcomings. First, the conventional BI method cannot effectively calibrate sensor drift faults. Second, the BI method relies on sensor fault detection and diagnosis (FDD). To address these shortcomings, this study proposed an improved BI (imBI) method that enhanced the capability of BI to calibrate drift faults by updating the calibration constant x. Moreover, sensor fault detection, diagnosis and calibration (FDDC) were conducted by combining Principal Component Analysis (PCA) with the imBI method (PCA-imBI). The proposed methods were validated through two case studies involving a chiller system and an air handling unit (AHU) system, having different degrees of bias faults and drift faults in temperature sensors, differential pressure sensors and flow sensors. The results demonstrated that the PCA-imBI method effectively achieved FDDC for both bias and drift faults. Considering the case studies, the average calibration rates for bias and drift faults reached 99.74 % and 95.29 %, respectively. For drift fault calibration, the imBI method outperformed the dynamic calibration method and PCA reconstruction method by 16.04 % and 4.31 %, respectively.