Historical data from maintenance work orders (MWOs) is a powerful source of information to improve maintenance decisions and procedures. However, data quality often impacts an analyst’s ability to calculate important Key Performance Indicators (KPIs) and analyze trends within a facility. Data quality can refer to the amount of missing data, accuracy of data, or availability of appropriate data fields. The end goal of an analysis dictates the task’s particular data quality requirements. When data quality is low, analysis accuracy is reduced and further insight is required from maintenance personnel familiar with the particular facility to interpret findings. This paper presents a case study of using historical MWO data to identify which HVAC assets are the best candidates for further monitoring, illustrating the impact of missing values from key data fields. The case study demonstrates approaches to analyzing an imperfect dataset using a Natural Language Processing (NLP) annotation software’s tagging system (known as Nestor) to perform structured data extraction from the MWO free text fields. These findings can be used to guide future data analysis workflows when faced with imperfect maintenance data. We demonstrate how incomplete data collection impacts a facility’s ability to use historical MWO data.
How to Cite
Data Quality, Maintenance Work Orders, Missing data, Key Performance Indicators, HVAC, Technical Language Processing
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