Fault prognosis of HVAC air handling unit and its components using hidden-semi Markov model and statistical process control
Energy and Buildings, ISSN: 0378-7788, Vol: 240, Page: 110875
2021
- 23Citations
- 26Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Air handling units are key sub-systems of heating, ventilation and air conditioning systems, which are used to condition air to satisfy human comfort requirements. Fault prognosis allows maintenance crews to identify the Remaining Useful Life (RUL) of a system, thus unexpected breakdowns are avoided, leading to a decrease in maintenance costs. To estimate RULs, a Hidden Semi-Markov Model (HSMM)-based method is proposed. To estimate states of HSMMs accurately, a revised scaled method is developed to guarantee that state estimates do not approximate to infinity. Additionally, a new discrete statistical process control method is developed to filter out false state estimates of HSMMs. To estimate RULs of components and systems accurately and effectively, a backward recursive method is developed to integrate HSMMs’ parameters of time-duration distributions for multiple failure modes to generate those of components and systems directly, thus low computational effort is achieved. Experimental results illustrate that the RULs of components/systems can be predicted by our method accurately in an efficient way.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0378778821001596; http://dx.doi.org/10.1016/j.enbuild.2021.110875; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85102629604&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0378778821001596; https://dx.doi.org/10.1016/j.enbuild.2021.110875
Elsevier BV
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