| DOI | Resolve DOI: https://doi.org/10.1080/09613218.2018.1459004 |
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| Author | Search for: Gunay, H. Burak; Search for: Shen, Weiming1; Search for: Yang, Chunsheng2 |
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| Affiliation | - National Research Council of Canada. Construction
- National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | building operations; building performance; diagnostics; facilities management; fault detection; HVAC systems; maintenance; operator logbooks; text-mining |
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| Abstract | Operators’ work order descriptions in computerized maintenance management systems (CMMS) represent an untapped opportunity to benchmark a facility’s maintenance and operation performance. However, it is challenging to carry out analytics on these large and amorphous databases. This paper puts forward a text-mining method to extract information about failure patterns in building systems and components from CMMS databases. The method is executed in three steps. Step 1 is pre-processing to convert work order descriptions into a mathematical form that lends itself to a quantitative lexical analysis. Step 2 is clustering to focus on interesting sections of a CMMS database that contain work orders about failures in building systems and components – rather than less interesting routine maintenance and inspection activities. Step 3 is association rule-mining to identify the coexistence tendencies among the terms of cluster of interest (e.g. coexistence of the terms ‘radiator’ and ‘leak’). This text-mining method is demonstrated by using two data sets. One data set was from a central heating and cooling plant with four boilers and five chillers; the other data set was from a cluster of 44 buildings. The results provide insights into per equipment breakdown of failure events, top system and component-level failure modes, and their occurrence frequencies. |
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| Publication date | 2018-04-30 |
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| Publisher | Taylor & Francis |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 23003958 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 7c420300-9790-4255-9b1b-bcefb642f8e8 |
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| Record created | 2018-08-24 |
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| Record modified | 2020-03-16 |
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