Handling fault detection and diagnosis in incomplete sensor measurements for BAS based HVAC system

Abstract

Due to the development of sensor networks, Building Automation System (BAS) based heating, ventilation, and air conditioning (HVAC) has gradually replaced traditional HVAC to improve indoor thermal comfort and decrease energy consumption. Because of inevitable faults in sensors or networks, the real-time sensor data may be incomplete with corrupted, lost or undetected missing values. Therefore, Improved Fireworks Algorithm (IFWA)-Long Short-Term Memory (LSTM) is proposed to recover incomplete measurements in BAS HVAC sensors. Because sensor data loss mostly results from incorrect control commands from the BAS system or sensor damage, there is a high probability of concurrent sensor fault. Therefore, Independent Component Analysis (ICA)-K-Nearest Neighbors (KNN) is proposed to monitor the sensor's healthy state under the measurements recovered. This paper discusses the possible causes of incomplete measurement in BAS HVAC sensors and simulates the distribution by experiment. The experiment analyzes the data recovery capability of IFWA-LSTM for different types of missing sensor data in different sensor measurement loss scenarios. The result shows that compared to the comparative methods, IFWA-LSTM achieves a 6.45 %–86.92 % improvement in recovery accuracy and a 0.0041 to 0.6340 improvement in correlation coefficients. Subsequently, it discusses the fault diagnosis rate of ICA-KNN under full measurements and incomplete measurement recovered by IFWA-LSTM. Overall, ICA-KNN achieves an average fault diagnosis rate of 99.86 % and an average fault alarm rate of 0 % under full measurement. When applied to incomplete measurement recovered by IFWA-LSTM, ICA-KNN exhibits an average fault diagnosis rate of 92.18 % and an average fault alarm rate of 1.22 %.