With the wide adoption of building automation system, and the advancement of data,
sensing, and machine learning techniques, data-driven fault detection and diagnostics …

Artificial intelligence has showed powerful capacity in detecting and diagnosing faults of
building energy systems. This paper aims at making a comprehensive literature review of …

In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …

With the advent of the era of big data, buildings have become not only energy-intensive but
also data-intensive. Data mining technologies have been widely utilized to release the …

The discovery of new materials is one of the driving forces to promote the development of
modern society and technology innovation, the traditional materials research mainly …

With the advancement of industrial systems, fault monitoring and diagnosis methods based
on the data-driven attract much attention in recent years. This kind of methods are widely …

Over the past two decades, one-class classification (OCC) becomes very popular due to its
diversified applicability in data mining and pattern recognition problems. Concerning to …

Abstract Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller
systems can lead to discomfort for the users, energy wastage, system unreliability and …

In practical chiller systems, applying efficient fault diagnosis techniques can significantly
reduce energy consumption and improve energy efficiency of buildings. The success of the …

In this era of Industry 4.0, there are continuing efforts to develop fault detection and
diagnosis methods that are fully autonomous; these methods are self-learning, with little or …