The purpose of this paper is to identify quality issues with using historical work order (WO) data from computerised maintenance management systems for reliability analysis; and develop an efficient and transparent process to correct these data quality issues to ensure data is fit for purpose in a timely manner.
This paper develops a rule-based approach to data cleansing and demonstrates the process on data for heavy mobile equipment from a number of organisations.
Although historical WO records frequently contain missing or incorrect functional location, failure mode, maintenance action and WO status fields the authors demonstrate it is possible to make these records fit for purpose by using data in the freeform text fields; an understanding of the maintenance tactics and practices at the operation; and knowledge of where the asset is in its life cycle. The authors demonstrate that it is possible to have a repeatable and transparent process to deal with the data cleaning activities.
How engineers deal with raw maintenance data and the decisions they make in order to produce a data set for reliability analysis is seldom discussed in detail. Assumptions and actions are often left undocumented. This paper describes typical data cleaning decisions we all have to make as a routine part of the analysis and presents a process to support the data cleaning decisions in a repeatable and transparent fashion.
The authors would like to acknowledge funding and support for this project from CRC Mining and its partner organisations.
Hodkiewicz, M. and Ho, M. (2016), "Cleaning historical maintenance work order data for reliability analysis", Journal of Quality in Maintenance Engineering, Vol. 22 No. 2, pp. 146-163. https://doi.org/10.1108/JQME-04-2015-0013Download as .RIS
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