化工学报2017，Vol.68Issue(3)：1099-1108,10.DOI:10.11949/j.issn.0438-1157.20161077
基于密度权重支持向量数据描述的冷水机组故障检测
Chiller fault detection by density weighted support vector data description
摘要
Abstract
False alarm rate (FAR) is a key indicator to evaluate performance of chiller fault detection methods, since customers cannot accept high FAR. In order to reduce FAR of support vector data description (SVDD)-based chiller fault detection, a density weighted support vector data description (DW-SVDD)-based chiller fault detection method was proposed by integration of density weight into SVDD with a consideration of density distribution of sample data in real space. The proposed method was validated with experimental data of RP-1043 ASHRAE and detection results were compared to those of traditional SVDD chiller fault detection methods. The results showed that the new method could reduce FAR from 10.5% to 7%, which was lowered about 30%, and had excellent detection performance for 7 typical chiller faults at 4 severity levels.关键词
支持向量数据描述/算法/集成/冷水机组/故障检测/模型Key words
support vector data description/algorithm/integration/chiller/fault detection/model分类
通用工业技术引用本文复制引用
顾笑伟,王智伟,王占伟,何所畏,闫增峰..基于密度权重支持向量数据描述的冷水机组故障检测[J].化工学报,2017,68(3):1099-1108,10.基金项目
"十二五"国家科技支撑计划项目(2011BAJ03B06). supported by "Twelfth Five-Year" National Key Technology Research and Development Program of China(2011BAJ03B06). （2011BAJ03B06）
