Veronica, D.
(2025),
ZandrEA Software in Research to Automate Fault Detection and Diagnostics of Mechanical Systems in Large Commercial Buildings -- A Primer, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.2337, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958096
(Accessed April 12, 2025)
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ZandrEA Software in Research to Automate Fault Detection and Diagnostics of Mechanical Systems in Large Commercial Buildings -- A Primer
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Abstract
The mechanical systems in commercial buildings, and especially their heating, ventilating, and air-conditioning (HVAC) systems, are routinely the subjects of significant energy waste and unnecessary carbon footprint due to operational malfunctions or improper control actions (''faults"). Larger-sized buildings have automatic control systems that continually generate large amounts of sampled data, offering an on-site resource that can be leveraged for automated fault detection and diagnosis (AFDD) done in real time. Currently marketed approaches to detecting faults using software are reviewed, with emphasis on how those are less effective compared to more autonomous and highly automated AFDD done in real time. Elements crucial to the viability of real-time AFDD include research advancing the technology of it, whether done by NIST or other entities, and the transfer of that innovative technology to the private sector, where marketable products and services can benefit from it. In pursuit of that end, NIST has founded an open-source software development project, ZandrEA, drawing together collaborators having a stake or an interest in AFDD advancement, including laboratories, academic institutions, businesses, and individuals. Further, NIST contributes to ZandrEA a foundational software component, described in detail here, such that this document is a primer that prospective collaborators can use for orientation and dialogue.Citation
Technical Note (NIST TN) - 2337
Report Number
2337
NIST Pub Series
Pub Type
NIST Pubs
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Keywords
afdd, automated fault detection diagnostics, Bayes theorem applied, Bayesian inference, expert system, fdd, hvac, intelligent buildings, open-source software, naive Bayes classifier.
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Created April 10, 2025