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Computerized maintenance management systems (CMMS) have become a widely used operational tool in commercial buildings. The tools’ stored data has the potential applications in operational benchmarking and planning. Despite the importance of data gathered for the mentioned purposes, research converting CMMS databases into key operational performance metrics is limited. Therefore, there is a necessity to develop a straightforward and automatic framework which facilitates this process regardless of the tool used. To address this gap, this study proposes a framework by employing a set of text-mining methods. This framework normalizes and cleans up the description of work-orders by various techniques. Then, the work-order descriptions and other alphanumeric data are processed so that important statistical information is organized and extracted. Subsequently, the generated information is sorted in a room- and floor-wise approach. The whole methodology is also employed on a case study building and it successfully categorizes work-orders by order types, temporal and spatial indicators, enabling continuous benchmarking of operational performance. The results showed that only 20 out of 126 rooms in the sample building account for nearly all work orders. Also, the highest and lowest numbers of work-orders occurred in June and December, respectively, and the mechanical issues are the main cause in these months.
Citation: 2021 Virtual Conference Papers
Product Details
- Published:
- 2021
- Number of Pages:
- 9
- File Size:
- 1 file , 790 KB
- Product Code(s):
- D-VC-21-C002
