基于SVDD的冷水机组传感器故障检测及效率分析  

SVDD-based chiller sensor fault detection method and its detection efficiency

作　　者：李冠男[1] 胡云鹏[1] 陈焕新[1] 黎浩荣 李炅[3] 胡文举[4] 

机构地区：[1]华中科技大学能源与动力工程学院,湖北武汉430074 [2]University of Nebraska-Lincoln,PKI-101 F,1110 S,67th street,Lincoln,NE USA 68182 [3]合肥通用机械研究院压缩机技术国家重点实验室,安徽合肥230031 [4]北京建筑大学供热供燃气通风及空调工程北京市重点实验室,北京100044

出　　处：《化工学报》2015年第5期1815-1820,共6页CIESC Journal

基　　金：国家自然科学基金项目(51328602);2013年压缩机技术国家重点实验室开放基金项目;供热供燃气通风及空调工程北京市重点实验室研究基金资助课题(NR2013K02)~~

摘　　要：传感器是制冷空调系统的重要组成部分,起着测量数据和监控状态的作用。传感器故障,尤其是输出偏差会引起测量值不准,影响控制策略,导致系统能耗增加。依据模式识别理论,故障检测可处理为一种单分类问题。据此采用一种单分类模式识别工具——支持向量数据描述(SVDD),针对冷水机组进行了偏差故障条件下的传感器故障检测工作。收集冷水机组实测正常运行数据,基于训练集建立SVDD模型,进行冷水机组传感器故障检测;在测试集中引入不同幅值水平的偏差故障,分析检测效率。结果表明:基于SVDD的冷水机组传感器故障检测效果明显,但对于不同传感器的不同幅值偏差故障,故障识别程度并不一致。In the refrigeration and air conditioning system, sensors are independent component for physical data measuring and operating state monitoring. Sensor faults, especially sensor biases output will lead to incorrect measurement, inappropriate controlling strategy and further energy consumption rise. Based on the pattern recognition theory, the fault detection task could be considered as a one-class classification problem. Therefore, a powerful pattern recognition-based one-class classification algorithm, Support Vector Data Description （SVDD） was used to detect the sensor biases occurring in a chiller system. The practical fault-free data were used as training dataset to develop the SVDD model so as to detect the sensor faults. The method and its fault detection efficiency were validated by test dataset with different artificially introduced levels of sensor biases. The SVDD-based fault detection method worked well with chiller practical operating measurements, but the fault detection efficiencies of different sensors with different level faults were inconsistent.

关 键 词：冷水机组 过程控制 故障检测 支持向量数据描述 算法 模型简化 

分 类 号：TB65[一般工业技术—制冷工程]