LAUSR

Abstracts Sensors play essential roles in industrial automatic control systems. The faulty or inaccurate sensors may cause uncomfortable thermal environments, shortened component lifetime and energy consumption loss. Considering the condition-adaptive issue of principal component analysis (PCA) models in fault diagnosis, a data-driven optimized statistical model applied for sensor fault detection and diagnosis (FDD) is proposed in the paper: the subtraction clustering and k-means clustering are combined to identify and classify modeling measurements of unsteady operating conditions. Sensor measurements from a real water source heat pump air-conditioning system is tested and the result shows that the clustering-based statistical model can enhance the ability of dealing with data of multiple operation conditions compared with the traditional PCA model; Different statistical indexes show sensitivity difference in detecting faults in the case of same sensors and same faults.