A practical solution for HVAC prognostics: failure mode and effects analysis in building maintenance

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DOIResolve DOI: http://doi.org/10.1016/j.jobe.2017.10.013
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TypeArticle
Journal titleJournal of Building Engineering
ISSN2352-7102
Volume15
Pages2632
SubjectFMEA; work-order; fault detection and diagnostics (FDD); prognostics; HVAC
AbstractFault detection, diagnostics, and prognostics (FDD&P) is attracting an amount of attention from building operators and researchers because it can help greatly improve the performance of building operations by reducing energy consumption for heating, ventilation and air-conditioning (HVAC) while improving occupant comfort at the same time. However, FDD&P, particularly HVAC prognostics, for building operations remains with many challenges due to special operation environments of HVAC systems. These challenges include “tolerance or ignorance” of failures in long-haul operations, lack of operation regulations, and even lack of documents for HVAC failure mode and effects analysis (FMEA), which is a systematic method of identifying and preventing system, product and process problems. To address some of these challenges, the authors propose an FMEA method for common building HVAC equipment by exploring work-orders generated by building energy management systems (BEMS) using a data mining approach. With this FMEA approach, it is possible for building operators to isolate and prognose faults practically. The FMEA approach also helps us tackle high impact failures, for which operation data can be acquired and machine learning-based predictive models can be developed. This paper reports some preliminary results in conducting an HVAC FMEA from a large number of work-orders obtained from a BEMS in routine operations. The HVAC FMEA will be used as a guidance tool for data gathering and developing data-driven models for HVAC FDD&P and as a practical solution for HVAC prognostics in case that predictive models are difficult to develop.
Publication date
PublisherElsevier
LanguageEnglish
AffiliationDigital Technologies; Construction; National Research Council Canada
Peer reviewedYes
NPARC number23002579
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Record identifier849805a5-9bc8-45b9-a9e9-0bb13aa710f2
Record created2017-12-01
Record modified2017-12-01
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