AHU sensor fault diagnosis in various operating conditions based on a hybrid data-driven model combined energy consumption

Abstract

Data-driven methods has the inspiring potential for sensors fault detection in heating, ventilation, and air-conditioning (HVAC) intelligence control system. However, the interpretability of pure data-driven methods is relatively poor. Moreover, pure data-driven methods heavily rely on quality of HVAC sensor data that causes pure data driven methods have inconsistent detection efficiency in various operation conditions and sensor locations, resulting in poor general applicability. To solve above problems, an enhanced data-driven model based on energy consumption correlation (ECC) is proposed. First, the collected data from sensors are collected in chronological order and undergo data preprocessing techniques for noise removal. Subsequently, the energy consumption data is utilized to differentiate the operating conditions of the HVAC system. Simultaneously, a mapping relationship is established between sensor data and energy consumption. It uses the mapping relationship to construct the ECC for faulty sensor detection. Finally, the diagnosis of air handling unit (AHU) sensor faults is achieved by employing the hybrid data-driven model based on the ECC. The experiment conducted a comparison between the hybrid data-driven model and two novel pure data-driven methods. The results of the experiment demonstrate that the hybrid data-driven model exhibits excellent fault detection efficiency across different operating conditions and sensor locations. The fault alarm rate of the hybrid data-driven model is 0–3.26% under various AHU sensor fault conditions. And the fault diagnosis rate of the hybrid data-driven model is 96.23% and 96.11% under single-operation condition and dual-operation condition, respectively. Furthermore, compared to pure data-driven methods, the hybrid data-driven model is less affected by data quality and possesses a broader applicability.