Fault Detection And Diagnosis In Air-source Heat Pump Air-conditioning System Using PCA Method

In modern society, along with the rise and great development of the intelligence buildings, the scale of HVAC systems is increasing greatly, and the equipments category and quantity are increasing numerously, failures arise inevitably. Owing to problems that arise at various stages of the building life cycle, from design planning to operation, many buildings routinely fail to perform as well as expected and satisfy performance expectations envisioned at design. The effective fault detection and diagnosis (FDD) system for HVAC systems can reduce energy consumption, maintain a comfortable indoor environment, and reduce equipment loss and the greenhouse gas emissions. Furthermore, such failures often go unnoticed for extended periods of time. Therefore, it is very necessary to carry out FDD research in HVAC systems.This paper proposes a sensor fault detection and diagnosis scheme, and a fault detection method that provides detection of condenser fouling fault of air-source heat pump water chiller/heater, using Principal Component Analysis (PCA) method on an air-source heat pump air-conditioning system. The basic processes of the PCA-based FDD include four steps: PCA model building process, data acquisition process, fault detection process and fault diagnosis process.PCA method decomposes the data space into Principal Component Subspace (PCS) and Residual Subspace (RS). In normal condition, the data are mainly located in PCS, while when there is fault occurs, the data will deviate PCS and the projection in RS will increase significantly. Thus we can detect whether there is fault occurs via detection of the data projection in RS. Squared Prediction Error (SPE) statistic is used as indicators of fault detection. This paper suggests: when SPE(x)≤δ2, the system is under normal operation condition; when SPE(x)>δ2, an abnormal condition exists. Fault reconstruction can find faulty measurement data corresponding to the estimated value of the normal process. This paper uses iterative method to carry out sensor fault reconstruction. The iterative reconstruction is the process gradually moving towards PCS along the fault direction. Then, Sensor Validity Index (SVI) is used to identify the faulty sensor. SVI is the SPE ratio before and after the fault recovery. If SVIj is close to 1, it is considered that the reconstruction direction is not the fault direction; if SVIj is close to 0, it is considered that the reconstruction direction is precisely the fault direction.Finally, this paper uses field tests data to verify the PCA methods. In the air-source heat pump air-conditioning system, a PCA model was used to carry out fault detection, identification and reconstruction for the bias, drifting and complete failure on the sensors. Meanwhile, another PCA model was used to detect the condenser fouling fault was detected by the PCA method. The results show that the PCA method is correct and effective.