期刊
APPLIED THERMAL ENGINEERING
卷 160, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2019.114098
关键词
Fault diagnosis; Principal component analysis; Cluster analysis; Sensor; Heat pump air-conditioning system
资金
- National Natural Science Foundation of China [51876070, 51576074]
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.
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