Machine Learning-Based Automated Fault Detection and Diagnostics in Building Systems

William Nelson () and Christopher Dieckert
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William Nelson: Department of Mechanical Engineering, Energy Systems Laboratory, Texas AM University, College Station, TX 77843, USA
Christopher Dieckert: Facilities and Energy Services, Texas AM University, College Station, TX 77843, USA

Energies, 2024, vol. 17, issue 2, 1-23

Abstract: Automated fault detection and diagnostics analysis in commercial building systems using machine learning (ML) can improve the building’s efficiency and conserve energy costs from inefficient equipment operation. However, ML can be challenging to implement in existing systems due to a lack of common data standards and because of a lack of building operators trained in ML techniques. Additionally, results from ML procedures can be complicated for untrained users to interpret. Boolean rule-based analysis is standard in current automated fault detection and diagnostics (AFDD) solutions but limits analysis to the rules defined and calibrated by energy engineers. Boolean rule-based analysis and ML can be combined to create an effective fault detection and diagnostics (FDD) tool. Three examples of ML’s advantages over rule-based analysis are explored by analyzing functional building equipment. ML can detect long-term faults in the system caused by a lack of system maintenance. It can also detect faults in system components with incomplete sets of sensors by modeling expected system operations and by making comparisons to actual system operations. An example of ML detecting a failure in a building is shown along with a demonstration of the soft decision boundaries of ML-based FDD compared to Boolean rule-based FDD analysis. The results from the three examples are used to demonstrate the strengths and weaknesses of using ML for AFDD analysis.

Keywords: fault detection; fault diagnosis; machine learning; building systems; HVAC; commercial building; case study (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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