Equipment management is one of the factors most critical to the success of construction projects. Being in charge of a large fleet, equipment managers for large contractors need to identify problems in equipment usage, repair, and maintenance based on large amounts of daily operational data. This paper presents a naïve outlier mining algorithm for the automatic screening and sorting of data sets based on the ways in which individual records deviate from their main bodies of data groups. The proposed outlier definition and outlier mining algorithm demonstrate better performance over the current well-known algorithms for real-world datasets. Application of the new algorithm to the proposed equipment analysis system is expected to provide reliable, quick solutions to problem detection in equipment databases.