Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76259
Title: Identification and isolation of outdoor fouling faults using only built-in sensors in variable refrigerant flow system : a data mining approach
Authors: Li, GN 
Hu, YP
Chen, HX
Wang, JY
Guo, YB
Liu, JY
Li, J
Keywords: Condenser air-side fouling
Data mining
Decision tree
Fault detection and diagnosis
Multiple linear regression
Variable refrigerant flow
Issue Date: 2017
Publisher: Elsevier
Source: Energy and buildings, 2017, v. 146, p. 257-270 How to cite?
Journal: Energy and buildings 
Abstract: The outdoor air-side fouling fault is almost inevitable for the practical variable refrigerant flow system. Fouling fault grows gradually and naturally causing increasingly energy penalty and performance degradation to the system. Implementing a proper and reliable fault detection and diagnosis strategy is crucial for practical systems keeping away from fouling fault and maintaining optimal operations. However, traditional model-based and data-driven methods cannot work in practical systems due to the lack of critical sensors and interpretation for model reliability, respectively. Therefore, this study proposes a data mining approach to identify and isolate fouling faults using only built-in sensors. Density-based spatial clustering of applications with noise(DBSCAN) is used for data pre-processing. The classification and regressiontree(CART)-based classifier is employed for fault detection. Based on Pearson's correlation analysis, a multiple linear regression(MLR)-based fault indicator is developed for fault isolation. A case study on a 29.8 kW cooling capacity R410A variable refrigerant flow system is used to validate the proposed strategy. Four levels of foulings are experimentally investigated under three cooling conditions. Results reveal that the proposed strategy correctly identifies 98% of fouling data using only three built-in sensor measurements. It also isolate fouling data from both normal and refrigerant charge fault data.
URI: http://hdl.handle.net/10397/76259
ISSN: 0378-7788
EISSN: 1872-6178
DOI: 10.1016/j.enbuild.2017.04.041
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