基于灰色聚类与SVDD的冷水机组健康状态评估
Health status assessment of chillers based on grey clustering and SVDD
摘要：
针对实际工业环境中数据获取受限、难以收集大量冷水机组样本的问题,提出了一种结合灰色聚类与支持向量数据描述(SVDD)的健康状态评估方法。首先,在冷水机组正常运行期间,采用正常状态样本构建SVDD模型;然后,基于模糊理论将故障样本到SVDD中心的相对欧氏距离映射为健康指数;最后,采用灰色聚类的方法对健康指数进行等级划分,从而实现对冷水机组健康状态的准确描述。采用ASHRAE RP-1043数据集及某大楼冷水机组的实际运行数据对该方法进行了验证。结果表明,在有限样本条件下,该方法能够有效评估冷水机组的健康状态,评估结果与冷水机组的实际健康状态较为一致。
Abstract：
Aiming at the problem that data acquisition is limited and it is difficult to collect a large number of the chiller’s samples in actual industrial environment, a health status assessment method combining grey clustering and support vector data description (SVDD) is proposed. First, during the normal operation of the chiller, the SVDD model is constructed based on normal state samples. Then, fuzzy theory is used to map the relative Euclidean distance from the fault samples to the SVDD center into a health index. Finally, the grey clustering method is used to classify the health indexes into levels, thereby achieving an accurate description of the health status of the chiller. The proposed method is validated using the ASHRAE RP-1043 dataset and the actual operating data from a building’s chillers. The results show that this method can effectively assess the health status of the chiller under the condition of limited samples, and the assessment results are consistent with the actual health status of the chiller.
Keywords:chiller; support vector data description; fuzzy theory; grey clustering; health status assessment