Abstract
A whole building fault (WBF) refers to a fault occurring in one component, but may cause impacts on other components or subsystems, or arise impacts of significant energy consumption and thermal comfort. Conventional methods which targeted at the component level fault detection cannot be successfully employed to detect a WBF because of the fault propagation among the closely coupled equipment or subsystems. Therefore, a novel data-driven method named weather and schedule-based pattern matching (WPM) and feature based principal component analysis (FPCA) method for WBF detection is developed. Three processes are established in the WPM-FPCA method to address three main issues in the WBF detection. First, a feature selection process is used to pre-select data measurements which represent a whole building's operation performance under a satisfied status, namely baseline status. Secondly, a WPM process is employed to locate weather and schedule patterns in the historical baseline database, that are similar to that from the current/incoming operation data, and to generate a WPM baseline. Lastly, PCA models are generated for both the WPM baseline data and the current operation data. Statistic thresholds used to differentiate normal and abnormal (faulty) operations are automatically generated in this PCA modeling process. The PCA models and thresholds are used to detect WBF. This paper is the first of a two-part study. Performance evaluation of the developed method is conducted using data collected from a real campus building and will be described in the second part of this paper.
High Dimensional Feature for Hyperspectral Image Classification
