期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 13, 期 3, 页码 1369-1380出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2016.2644669
关键词
Air-handling unit (AHU); data-driven method; fault detection and diagnosis (FDD); feature selection; optimal sensor configuration
类别
资金
- Republic of Singapore's National Research Foundation under Campus for Research Excellence and Technological Enterprise (CREATE) program
- University of California, Berkeley, as a center for intellectual excellence in research and education in Singapore
Experiments show that operation efficiency and reliability of buildings can greatly benefit from rich and relevant datasets. More specifically, data can be analyzed to detect and diagnose system and component failures that undermine energy efficiency. Among the huge quantity of information, some features are more correlated with the failures than others. However, there has been little research to date focusing on determining the types of data that can optimally support fault detection and diagnosis (FDD). This paper presents a novel optimal feature selection method, named information greedy feature filter (IGFF), to select essential features that benefit building FDD. On one hand, the selection results can serve as reference for configuring sensors in the data collection stage, especially when the measurement resource is limited. On the other hand, with the most informative features selected by the IGFF, the performance of building FDD could be improved and theoretically justified. A case study on air-handling unit (AHU) is conducted based on the dataset of the ASHRAE Research Project 1312. Numerical results show that, compared with several baselines, the FDD performances of conventional classification methods are greatly enhanced by the IGFF.
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