Handling Incomplete Sensor Measurements in Fault Detection and Diagnosis for Building HVAC Systems

Abstract

Due to the development of sensor networks and information technology, data-driven fault detection and diagnosis (FDD) has been made possible with real-time multiple sensor measurements. However, due to inevitable sensor errors or communication failures, the raw data are usually incomplete with corrupted values, lost values, or undetected missing values. In practice, the incomplete data are usually dealt with by directly excluding incomplete measurements and abnormal spikes. In addition, some preprocessing methods, which naively impute data though averaging or smoothing, have also been widely applied. In this article, we address the building FDD problem with incomplete data by proposing a new approach, the adjacent information recovery (AIR) filter. The AIR filter is utilized to deal with the FDD for a typical air handling unit (AHU) system with incomplete data based on the ASHRAE Research Project 1312. Experimental results show that the proposed method improves FDD performance by recovering missing sensor measurements and outperforms the state-of-the-art methods. Note to Practitioners —Fault detection and diagnosis (FDD) for smart buildings by addressing the fact that FDD systems are of great importance for saving energy and improving occupancy comfort levels and building safety levels. Existing FDD methods are mainly based on the assumption that sensor data are complete and reliable, which are rarely true in real practice. To solve the building FDD problem with incomplete data, in this article, the adjacent information recovery (AIR) filter is proposed to recover the missing data before applying FDD methods. The AIR filter takes the time series adjacency information into consideration via hidden Markov model (HMM) and includes the channel adjacency information with the collaborating filtering technique.