Sensor fault detection and diagnosis for a water source heat pump air-conditioning system based on PCA and preprocessed by combined clustering. (September 2019)

• Record Type: Journal Article
• Authors: Zhang, Hongtao; Chen, Huanxin; Guo, Yabin; Wang, Jiangyu; Li, Guannan; Shen, Limei
• Is Part Of: Applied thermal engineering. Volume 160(2019)
• Publication Date: 2019-09
• Subjects: Fault diagnosis -- Principal component analysis -- Cluster analysis -- Sensor -- Heat pump air-conditioning system; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2019.114098
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: The fault diagnosis models based on PCA and clustering method are established. Combined K-means and subtractive clustering is applied for classifying sensor measurements by various operation conditions. The quantified index of Q diagnosis ratio is come up with to describe the ability of identifying the faulty sensors. 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.