Influence of Three Sizes of Sliding Windows on Principle Component Analysis Fault Detection of Air Conditioning Systems

• 作者: YANG Xuebin MA Yanyun HE Ruru WANG Ji LUO Wenjun
• 来源期刊: 东华大学学报（英文版）
• 学科: 工学
• 年，卷（期）: 2022,（1）
• 所属期刊栏目: Resource, Ecology and Environment
• 页码范围: 72-78
• 页数: 7页
• 分类号: TU83|G640
• 语种: 英文
• DOI: 10.19884/j.1672-5220.202103002

摘要

Principal component analysis ( PCA ) has been already employed for fault detection of air conditioning systems. The sliding window, which is composed of some parameters satisfying with thermal load balance, can select the target historical fault-free reference data as the template which is similar to the current snapshot data. The size of sliding window is usually given according to empirical values, while the influence of different sizes of sliding windows on fault detection of an air conditioning system is not further studied. The air conditioning system is a dynamic response process, and the operating parameters change with the change of the load, while the response of the controller is delayed. In a variable air volume ( VAV) air conditioning system controlled by the total air volume method, in order to ensure sufficient response time, 30 data points are selected first, and then their multiples are selected. Three different sizes of sliding windows with 30, 60 and 90 data points are applied to compare the fault detection effect in this paper. The results show that if the size of the sliding window is 60 data points, the average fault-free detection ratio is 80. 17％ in fault-free testing days, and the average fault detection ratio is 88. 47％ in faulty testing days.