摘要
冷水机组作为复杂系统,其变量间相关性严重,并且故障时的症状和原因具有多样性,导致了冷水机组的故障诊断较为困难.为了降低数据冗余性,提高故障诊断效率,提出一种基于独立元分析与最小二乘支持向量机相结合的冷水机组故障诊断方法.首先,运用独立元分析法提取冷水机组变量的独立元信息;然后,将提取的独立元信息作为最小二乘支持向量机的输入值进行故障类型的识别.利用北京某高校的地铁车站通风空调实训平台的实验数据验证该模型的故障诊断性能,并与传统的冷水机组故障诊断方法进行对比.比较结果证明基于独立元分析与最小二乘支持向量机相结合的冷水机组故障诊断方法优于传统方法.这表明该方法可以有效提取数据的高阶统计信息,提高故障诊断的效率.
Chiller is a complex system in which the correlation between variables is serious. When a fault occurs , the symptoms and causes of the chiller show diversity, leading to great difficulty in fault diagnosis of the chiller. To reduce the data redundancy and improve the efficiency of the fault diagnosis, a fault diagnosis method for chiller based on independent component analysis （ICA） and least squares support vector machine （LSSVM） was proposed. ICA was used to extract the correlation of variables of the chiller and feature extraction was made which was served as input parameters of LSSVM in order to identify the chiller’s fault type. The method was validated by using the laboratory data from the ventilation and air conditioning training platform of a subway station in Beijing and the method was compared with the traditional fault diagnosis method of chiller. Results show that the method is better than the traditional method. It can effectively extract the data from the high-order statistical information and improve the efficiency of fault diagnosis.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2017年第11期1641-1647,共7页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(61174109
61364009)
关键词
冷水机组
故障诊断
独立元分析
最小二乘支持向量机
chiller
fault diagnosis
indepedent component analysis （ICA）
least squares support vector machine （ LSSVM）
