As one of the multivariate data analysis methods

principal component analysis (PCA) is widely used for sensor fault diagnosis in refrigeration and air conditioning systems. First

the 18 sensors commonly used in a variable refrigerant flow (VRF) system are selected to establish sensor fault detection and diagnosis (FDD) models according to the thermal equilibrium principles and control logics of the system. Then

the process of sensor FDD is presented with the Q statistic and Q contribution as test standards

combined with the principles of a PCA algorithm. Next

validation is conducted using the measured data after introducing sensor faults of different types and degrees. Finally

the characteristics of sensor FDD are obtained under different fault conditions. As a whole

the results prove the reliability of applying a PCA to the sensor FDD process for VRF systems. Specific performance characteristics are as follows: fault detection efficiency has big differences for different sensors under different types and extents of faults; the fault detection efficiency of the PCA-based sensor fault detection method under the conditions with small deviation faults is low; and for individual sensors

the fault detection efficiency is integrally low. Since fault diagnosis is based on fault detection

the above-mentioned fault detection method may play important role in the FDD process.