文章基本信息

标题：Fault identification for chiller sensor based on partial least square method
本地全文：下载
作者：Bang Wu ; Yunpeng Hu ; Chuanhui Zhou 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2021
卷号：233
页码：3057
DOI：10.1051/e3sconf/202123303057
出版社：EDP Sciences
摘要：Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.
其他摘要：Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.