Fault detection, diagnosis and data recovery for a real building heating/cooling billing system

A method of fault detection, diagnosis (FDD) and data recovery is proposed for building heating/cooling billing system in this paper. Principal component analysis (PCA) approach is used to extract the correlation of measured variables in heating/cooling billing system and reduce the dimension of measured data. The measured data of billing system under normal operating condition are used to build PCA model. Sensor faults of bias, drifting and complete failure are introduced to building heating/cooling billing system for detection and identification. Square prediction error (SPE) statistic is used to detect sensor faults in the system. Then, sensor validity index (SVI) was employed to identify faulty sensors. Finally, a reconstruction algorithm is presented to recover the correct data of faulty sensor in accordance with the correlations among system variables. A program for the FDD, and data recovery method is developed and employed in the heating/cooling billing system of a real small-scale laboratory building to test its applicability and effectiveness. Validation results show that the proposed FDD and data recovery method is correct and effective for most faults in building heating/cooling billing system. (C) 2009 Elsevier Ltd. All rights reserved.