基于递推PCA的变工况中央空调系统故障诊断

Fault diagnosis of HVAC in variable operating mode based on recursive PCA

由于节能以及用户需求等原因,中央空调系统(HVAC)设定温度和风量等工况时常发生改变,这会导致系统模态发生变化,给故障诊断增加难度。为此开展了中央空调变工况下的故障诊断方法研究,首先为了准确模拟HVAC系统变工况及其典型故障,通过专用建筑能源模拟器TRNSYS软件进行建模,并实时采集HVAC系统各传感器数据,随后针对传统PCA算法模型无法适应系统工况变化,容易出现大量误报的问题,发展一种递推主元分析(RPCA)方法,通过利用传感器输出的新数据在线更新原始的PCA模型,即对数据矩阵的均值、方差等进行更新,解决了HVAC系统变工况时参数动态变化所引发的误报的问题,最后基于TRNSYS和MATLAB联合仿真,验证了所提方法的有效性和优越性。

Due to energy conservation and user needs,the set temperature and air volume of HVAC often change,which can lead to operating mode change and thus increase the difficulty of fault diagnosis.In this paper,research on fault diagnosis methods for HVAC under variable operating mode is carried out.Firstly,in order to accurately simulate the variable operating mode and typical faults of the HVAC system,a dedicated building energy simulator software TRNSYS is used for modeling of HVAC in various operating modes.Secondly,considering that the traditional PCA algorithm model,once established,could not be updated online thus cannot deal with the changes in system operating modes and generally leads to a large number of false alarms,a recursive principal component analysis(RPCA) method is developed for fault diagnosis of HVAC in varying operating modes to reduce false alarm by updating key parameters including mean and variance online.Finally,the effectiveness and superiority of the proposed method are verified by the joint simulation of TRNSYS and MATLAB.