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Text-mining building maintenance work orders for component fault frequency.
- Source :
- Building Research & Information; Jul2019, Vol. 47 Issue 5, p518-533, 16p, 1 Black and White Photograph, 4 Diagrams, 3 Charts, 8 Graphs
- Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09613218
- Volume :
- 47
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Building Research & Information
- Publication Type :
- Academic Journal
- Accession number :
- 133290237
- Full Text :
- https://doi.org/10.1080/09613218.2018.1459004