Sensor data validation and fault diagnosis using Auto-Associative Neural Network for HVAC systems

Abstract

The Heating, Ventilation, and Air conditioning (HVAC) system is a major system in buildings for conditioning the indoor environment. Sensor data validation and fault diagnosis for HVAC systems are essentially important to secure a reliable and efficient operation since sensor measurements are vital for the HVAC closed-loop control system. The aim of this work is to address this matter by developing a data-driven approach using the system's normal operation data and without the need for the knowledge of the mathematical model of the system. It is based on an Auto-Associative Neural Network (AANN) that is structured and trained to construct an input-output mapping model based on data dimensionality reduction that is capable of validating sensor measurements in terms of sensor error correction, missing data replacement, noise filtering, and inaccuracy correction. It can be used for both single and multiple sensor faults diagnosis by monitoring the consistency between the actual and the AANN-estimated sensor reading. The validation of the proposed method is demonstrated on data obtained from a 3-zone HVAC system simulated in TRNSYS. The evaluation results show the effectiveness of the proposed approach and an improvement in terms of data validation and diagnostic accuracy when compared with a PCA-based method.