Multicondition operation fault detection for chillers based on global density-weighted support vector data description

Fault detection is essential to maintain the healthy and efficient operation of chillers. However, in actual chiller operation, due to fluctuating operating conditions, fault detection becomes very difficult, i.e., a low detection accuracy and high false alarm rate (FAR) are attained. To better reflect the distribution of data, effectively improve the detection accuracy and reduce the FAR, this study considers the global perspective of target data, and a method based on global density-weighted support vector data description (GDW-SVDD) is proposed to detect chiller faults. This method is validated against ASHRAE RP-1043 experimental data. The results show that compared to the conventional SVDD method, the GDW-SVDD method not only effectively reduces the FAR from 10.75% to 8.25% but also improves the fault detection accuracy by 3.75% (under the reduced evaporator water flow fault at severity level 2). Compared to the density-weighted support vector data description (DW-SVDD) method, this method can effectively improve the accuracy of fault detection up to 6.5% (under the excessive oil fault at severity level 2) while simultaneously maintaining the same low FAR. Therefore, the proposed method is effective for chiller fault detection. (C) 2021 Published by Elsevier B.V.

Automated summary

This study proposes a method based on global density-weighted support vector data description (GDW-SVDD) to detect chiller faults, which can effectively improve fault detection accuracy and reduce false alarm rate. Experimental results show that compared to conventional methods and other methods, this approach has significant advantages in fault detection accuracy.