Intelligence in Industry 4.0 has led to the development of smart buildings with various control
systems for data collection, efficient optimization, and fault detection and diagnosis (FDD) …

Purely data-driven modeling methods exhibit inherent “black box” characteristics when
applied to the air source heat pump (ASHP) systems for energy efficiency improvement …

Artificial intelligence (AI), such as machine learning (ML) models, is profoundly impacting an
organization's ability to assess safety risks during the construction of tunnels. Yet, ML …

Although deep learning models have been rapidly developed, their practical applications
still lag behind for building energy systems (BESs) fault diagnosis. Owing to the “black-box” …

The cross-domain fault diagnosis (CDFD) method can provide accurate fault diagnosis
models for HVAC systems in the case of incomplete labeled data. However, the relationship …

Large language models (LLMs) have exhibited great potential in fault diagnosis of heating,
ventilation, and air conditioning systems. However, the fault diagnosis accuracy of LLMs is …

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 …

In the era of big data, data-driven models have become the most promising fault detection
and diagnosis solutions to building energy systems, due to their high accuracy and good …

Deep learning-based AI technology has the inspiring potential for smart management in
energy system of smart city. However, deep learning model is not efficient for the untrained …

The simultaneous occurrence of sensor fault and thermal fault results in misdiagnosis of the
current single-type fault diagnosis methods, and significantly reduces the diagnosis …