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

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Published Sep 22, 2019
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 solutions, problems, 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
PHM communities.

How to Cite

Navinchandran, M., Sharp, M. E., Brundage, M. P., & Sexton, T. B. (2019). Studies to Predict Maintenance Time Duration and Important Factors From Maintenance Workorder Data. Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.792
Abstract 466 | PDF Downloads 135

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Keywords

Maintenance, Neural Networks, Manufacturing, Maintenance Work Orders

Section
Technical Papers