Fault detection and diagnosis (FDD) are crucial aspects of maintaining efficient and energy-
saving heating ventilation and air conditioning (HVAC) systems. Conditions such as …

Recent studies show that artificial intelligence (AI), such as machine learning and deep
learning, models can be adopted and have advantages in fault detection and diagnosis for …

Fault diagnosis of the chiller is essential to guarantee chiller's safe operation and reduce
building energy consumption. However, the existing fault diagnosis methods rarely consider …

The failure of air conditioning systems is random and uncertain, with one or more faults
occurring simultaneously at any given time. Factors such as difficulty in collecting fault data …

Slow feature analysis (SFA) has achieved the successful applications in the chemical
process fault detection field. However, the basic SFA omits the process nonlinearity and …

The development of interpretable deep learning methods has garnered attention in fault
diagnosis. However, the effectiveness of various interpretation methods based on different …

Deep learning (DL), especially convolutional neural networks (CNNs), has been widely
applied in air handling unit (AHU) fault diagnosis (FD). However, its application faces two …

In the heating, ventilation and air conditioning (HVAC) system, the fault diagnosis of the air
handling unit (AHU) is critical to ensure the proper operation of the whole system. The AHU …

Refrigerant charging discrepancies constitute the predominant malfunctions in air
conditioning systems. Achieving the optimal charging level is crucial for system …

This study addresses the limitations in data-driven PM2. 5 concentration prediction, which
typically depends on statistical relationships with other factors, posting challenges in …