Using Weather and Schedule-Based Pattern Matching and Feature-Based Principal Component Analysis for Whole Building Fault Detection—Part I Development of the Method

Abstract

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 significant impacts on energy consumption and thermal comfort. Conventional methods (such as component level rule-based method or physical model-based method) which targeted at component level fault detection cannot be successfully used 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 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. Second, a WPM process is used to locate weather and schedule patterns in the historical baseline database, which are similar to that from the current/incoming operation data, and to generate a WPM baseline. Lastly, real-time 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 the 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.