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 …

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 …

Faults occurring in improper routine operations and poor preventive maintenance of heating,
ventilating, air conditioning, and refrigeration systems (HVAC&R) equipment result in …

Part II of this article will be published in Volume 11, Number 2, April 2005. Poorly
maintained, degraded, and improperly controlled equipment wastes an estimated 15% to …

Buildings are responsible for 40% of global energy use and contribute towards 30% of the
total CO2 emissions. The drive to reduce energy use and associated greenhouse gas …

This paper is the second of a two-part review of methods for automated fault detection and
diagnostics (FDD) and prognostics whose intent is to increase awareness of the HVAC&R …

Modern data-driven fault detection and diagnosis (FDD) techniques show impressive high
diagnostic accuracy in recognizing various air handling units (AHUs) faults. Most existing …

In this work, we develop models and a fault detection and isolation (FDI) methodology for
heating, ventilation and air conditioning (HVAC) systems that utilizes recurrent neural …

Data-driven methods for fault detection and diagnosis of air handling units (AHUs) have
attracted wide attention as they do not require high-level expert knowledge of the system of …

Stack aerosols are generated within vertical building drainage stacks during the discharge
of wastewater containing feces and exhaled mucus from toilets and washbasins. Fifteen …