Studies to Predict Maintenance Time Duration and Important Factors From Maintenance Workorder Data

Abstract

Maintenance Work Orders (MWOs) are a useful way ofrecording semi-structured information regarding maintenanceactivities in a factory or other industrial setting. Analysisof these MWOs could provide valuable insights regardingthe many facets of reliability, maintenance, and planning.Information such as which maintenance activities consumethe most work hours, identification of problem machines,and spare parts needs can all be inferred to some degreefrom well documented MWOs. However, before one canderive insights, it is first necessary to transform the data inthe MWOs (generally some form of natural language) intosomething more suitable for computer analysis. NIST previouslydeveloped a computer aided tagging system that allowsfor the quick identification of key concepts within the naturallanguage of the MWOs, and a protocol for categorizingthese concepts as solutions, problems, or items. Using thisannotation method, this paper investigates machine learningmethods to gain insights about work hours needed for variousmaintenance activities. Through these methods, it ispossible to explain the factors captured in the MWOs thathave the strongest relationship with the duration of maintenanceactions. The workflow of this research is to firstbuild strong data driven models to classify the duration ofany maintenance activity based on the language and conceptsgathered from the associated MWO. Sensitivity analysis ofthe inputs to these classifiers can then be used to determinerelationships and factors influencing maintenance activities.This paper investigates two machine learning models - aneural network classifier and a decision tree classifier. Inputfeatures for the classifier were the annotated concept tags for solutions, problems and items derived from MWOs of anactual manufacturer. This process for gaining insights can begeneralized to various applications in the maintenance andPHM communities.