ABSTRACT

Facility managers can significantly benefit from operational data, such as maintenance requests, stored in computerized maintenance management systems (CMMSs). This data is a valuable means to assess building performance and gain insights for preventive maintenance actions. However, databases are not always organized in such a way that allow undertaking analytics, therefore resulting in troubles when trying to generate useful information from raw data. This paper presents two methods based on a text-mining approach to extract valuable information from textual maintenance requests. The first method aims to extract the room identifier (ID) numbers where faults mainly occur, while the second one aims to identify the most problematic building elements and systems. The text-mining-based methods were tested by using a data set which contains 12,655 maintenance requests derived from a cluster of 33 buildings managed by the local administration of the Municipality of Trieste (Italy).

摘要

设施管理人员可以从存储在计算机化维护管理系统 (CMMS) 中的操作数据（例如维护请求）中受益匪浅。这些数据是评估建筑性能和深入了解预防性维护行动的宝贵手段。然而，数据库的组织方式并不总是允许进行分析，因此在尝试从原始数据生成有用信息时会出现问题。本文提出了两种基于文本挖掘方法从文本维护请求中提取有价值信息的方法。第一种方法旨在提取主要发生故障的房间标识符 (ID) 编号，而第二种方法旨在识别最有问题的建筑元素和系统。通过使用包含 12 个数据集的数据集测试基于文本挖掘的方法，