2018/04/30 by H. Burak Gunay, Weiming Shen, Chunsheng Yang · 1 citation
Business, Management and Accounting · Computer Science · #Big Data and Business Intelligence #Data Mining Algorithms and Applications #Rough Sets and Fuzzy Logic
paper · doi:10.1080/09613218.2018.1459004
openalex publication_date 2018/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.