Analysis of sensor fault detection in chiller based on PCA method

被引：0

作者：

机构：

[1] School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei

[2] Beijing Key Laboratory of Heating, Gas Supply, Ventilating and Air Conditioning Engineering, Beijing University of Civil Engineering and Architecture

来源：

Chen, H. (chenhuanxin@tsinghua.org.cn) | 1600年 / Materials China卷 / 63期

关键词：

Chiller; Detection efficiency; Fault detection; Principal component analysis;

D O I：

10.3969/j.issn.0438-1157.2012.z2.016

中图分类号：

学科分类号：

摘要：

Chiller is a highly nonlinear complex system. The sensor fault in the automation system of chiller will cause it to operate in unmoral situation and waste energy. Based on the principal component analysis (PCA) method, one of the analytical methods in multivariate statistics, the data in normal condition have been used to establish the train matrix. The square prediction error (SPE) has been used to analyze the sensor fault. The detection efficiency has been analyzed by the PCA method with introducing different level faults into the chiller. The result shows that PCA-based fault detection method works well, but the fault detection efficiencies of different sensors with different level faults are inconsistent. © All Rights Reserved.

引用

页码：85 / 88

页数：3

共 16 条

[1]

Kao J.Y., Pierce E.T., Sensor errors: Their effects on building energy consumption, ASHRAE Journal, 25, 12, pp. 42-45, (1983)

[2]

Wang S.W., Xiao F., AHU sensor fault diagnosis using principal component analysis method, Energy and Buildings, 36, 2, pp. 147-160, (2004)

[3]

Jiang Y., Zhu W., Sensor fault detection in heating, ventilation and air-conditioning systems, Journal of Tsinghua University, 39, 12, pp. 54-56, (1999)

[4]

Sun Y., Wang S., Huang G., Online sensor fault diagnosis for robust chiller sequencing control, International Journal of Thermal Sciences, 49, 3, pp. 589-602, (2010)

[5]

Wang S.W., Wang J.B., Law-based sensor fault diagnosis and validation for building air-conditioning systems, HVAC&R Research, 5, 4, pp. 353-380, (1999)

[6]

Wang H.T., Chen Y.M., Chan C.W.H., Et al., An online fault diagnosis tool of VAV terminals for building management and control systems, Automation in Construction, 22, pp. 203-211, (2012)

[7]

Katipamula S., Brambley M.R., Methods for fault detection, diagnostics, and prognostics for building systems - A review(I), HVAC&R Research, 11, 1, pp. 3-25, (2005)

[8]

Wang S.W., Chen Y.M., Fault-tolerant control for outdoor ventilation air flow rate in buildings based on neural network, Building and Environment, 37, 7, pp. 691-704, (2002)

[9]

Xiao F., Wang S.W., Xu X.H., Et al., An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems, Applied Thermal Engineering, 29, 4, pp. 712-722, (2009)

[10]

Wang S.W., Zhou Q., Xiao F., A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults, Energy and Buildings, 42, 4, pp. 477-490, (2010)

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