@inproceedings{9b0a66c1e0c3402fb748fc547c58394f,
title = "Whole building system fault detection based on weather pattern matching and PCA method",
abstract = "Multivariate statistical process analysis (MSPA) methods have been widely employed for component level fault detection in buildings. An MSPA method named as weather pattern matching (PM) and principal component analysis (PCA) method is proposed for whole building system fault detection. This method is modified from a component level fault detection method which is proved effective in detecting faults in air handling unit (AHU) and variable air volume (VAV) terminal. In the proposed method, Symbolic Aggregate approximation (SAX) method is employed to find similar weather pattern in historical database to accurately generate dynamic baseline dataset for PCA model to detect system faults. One real building data is used to evaluate the effectiveness of the proposed method.",
author = "Yimin Chen and Jin Wen",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 3rd IEEE International Conference on Control Science and Systems Engineering, ICCSSE 2017 ; Conference date: 17-08-2017 Through 19-08-2017",
year = "2017",
month = oct,
day = "26",
doi = "10.1109/CCSSE.2017.8088030",
language = "English (US)",
series = "2017 3rd IEEE International Conference on Control Science and Systems Engineering, ICCSSE 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "728--732",
booktitle = "2017 3rd IEEE International Conference on Control Science and Systems Engineering, ICCSSE 2017",
address = "United States",
}