Skip to main content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

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

Published

Author(s)

Fnu Madhusudanan Navinchandran, Michael E. Sharp, Michael P. Brundage, Thurston B. Sexton

Abstract

Maintenance Work Orders (MWOs) are a useful way of recording semi-structured information regarding maintenance activities in a factory or other industrial setting. Analysis of these MWOs could provide valuable insights regarding the many facets of reliability, maintenance, and planning Information such as which maintenance activities consume the most work hours, identification of problem machines, and spare parts needs can all be inferred to some degree from well-documented MWOs. However, before one can derive insights, it is first necessary to transform the data in the MWOs (generally some form of natural language) into something more suitable for computer analysis. NIST previously developed a computer aided tagging system that allows for the quick identification of key concepts within the natural language of the MWOs, and a protocol for categorizing these concepts as problems, solutions, or items. Using this annotation method, this paper investigates machine learning methods to gain insights about work hours needed for various maintenance activities. Through these methods, it is possible to explain the factors captured in the MWOs that have the strongest relationship with the duration of maintenance actions. The workflow of this research is to first build strong data-driven models to classify the duration of any maintenance activity based on the language and concepts gathered from the associated MWO. Sensitivity analysis of the inputs to these classifiers can then be used to determine relationships and factors influencing maintenance activities. This paper investigates two machine learning models - a neural network classifier and a decision tree classifier. Input features for the classifier were the annotated concept tags for solutions, problems and items derived from MWOs of an actual manufacturer. This process for gaining insights can be generalized to various applications in the maintenance and Prognostics and Health Management (PHM) communities.
Proceedings Title
Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019
Conference Dates
September 21-26, 2019
Conference Location
Scottsdale, AZ
Conference Title
Annual Conference of the Prognostics and Health Management Society 2019

Keywords

maintenance workorder, machine learning, predictive model, data analysis
Created September 26, 2019, Updated October 1, 2019