FDD in Building Systems Based on Generalized Machine Learning Approaches Academic Article

abstract

• Automated fault detection and diagnostics in building systems using machine learning (ML) can be applied to commercial buildings and can result in increased efficiency and savings. Using ML for FDD brings the benefit of advancing the analytics of a building. An automated process was developed to provide ML-based building analytics to building engineers and operators with minimal training. The process can be applied to buildings with a variety of configurations, which saves time and manual effort in a fault analysis. Classification analysis is used for fault detection and diagnostics. An ML analysis is defined which introduces advanced diagnostics with metrics to quantify a faults impact in the system and rank detected faults in order of impact severity. Explanations of the methodology used for the ML analysis include a description of the algorithms used. The analysis was applied to a building on the Texas A&M University campus where the results are shown to illustrate the performance of the process using measured data from a building.

authors

• Culp, Charles

published proceedings

• ENERGIES

author list (cited authors)

• Nelson, W., & Culp, C.

citation count

• 0

complete list of authors

• Nelson, William||Culp, Charles

publication date

• 2023

publisher

• MDPI  Publisher

published in

• Energies  Journal

keywords

• Building Systems
• Fault Detection
• Fault Diagnosis
• Hvac
• Machine Learning

Digital Object Identifier (DOI)

• 10.3390/en16041637

start page

• 1637

end page

• 1637

volume

• 16

issue

• 4

URL

• http://dx.doi.org/10.3390/en16041637