基于密度权重支持向量数据描述的冷水机组故障检测

doi:10.11949/j.issn.0438-1157.20161077

化工学报 ›› 2017, Vol. 68 ›› Issue (3): 1099-1108.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

顾笑伟¹, 王智伟¹, 王占伟¹, 何所畏¹, 闫增峰²

1 西安建筑科技大学环境学院, 陕西西安 710055;
2 西安建筑科技大学建筑学院, 陕西西安 710055

收稿日期:2016-08-01 修回日期:2016-11-04 出版日期:2017-03-05 发布日期:2017-03-05
通讯作者: 王智伟,wzhiwei-atu@163.com
基金资助:
“十二五”国家科技支撑计划项目（2011BAJ03B06）。

Chiller fault detection by density weighted support vector data description

GU Xiaowei¹, WANG Zhiwei¹, WANG Zhanwei¹, HE Suowei¹, YAN Zengfeng²

1 School of Environmental, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China;
2 School of Architecture, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China

Received:2016-08-01 Revised:2016-11-04 Online:2017-03-05 Published:2017-03-05
Contact: 10.11949/j.issn.0438-1157.20161077
Supported by:
supported by "Twelfth Five-Year" National Key Technology Research and Development Program of China (2011BAJ03B06).

摘要/Abstract

摘要：

虚警率（FAR）是评价冷水机组故障检测性能的关键指标，用户无法接受过高的FAR。为了降低支持向量数据描述（SVDD）在冷水机组故障检测时的FAR，将密度权重集成到SVDD中，提出了一种基于密度权重支持向量数据描述（DW-SVDD）的冷水机组故障检测方法，该方法考虑了样本数据在真实空间中的密度分布情况。使用ASHRAE RP-1043冷水机组实验数据对提出的方法进行验证，并将检测结果与传统SVDD的冷水机组故障检测方法进行比较。结果表明，提出的方法将FAR从10.5%降低到7%，同比下降超过了30%，同时对4个劣化等级下的7种典型冷水机组故障有着优良的检测性能。

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

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.

Key words: support vector data description, algorithm, integration, chiller, fault detection, model

中图分类号:

TB65

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.

GU Xiaowei, WANG Zhiwei, WANG Zhanwei, HE Suowei, YAN Zengfeng. Chiller fault detection by density weighted support vector data description[J]. CIESC Journal, 2017, 68(3): 1099-1108.

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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

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