Work orders - value from structureless text in the era of digitisation

Erik Salo, David McMillan, Richard Connor

Research output: Contribution to conferencePaper

Abstract

Free text and hand-written reports are losing ground to digitization fast, however many hours of effort are still lost across the industry to the manual creation and analysis of these data types. Work orders in particular contain valuable information from failure rates to asset health, but at the same time present operators with such analytical difficulties and lack of structure that many are missing out on the value completely. This research challenges the current mainstream practice of manual work order analysis by presenting a methodology fit for today’s context of efficiency and digitization.
A prototype text mining software for work order analysis was developed and tested in a user-oriented approach in cooperation with industrial partners. The final prototype combines classical machine learning methods, such as hierarchical clustering, with the operator’s expert knowledge obtained via an active learning approach. A novel distance metric in this context was adapted from information-theoretical research to improve clustering performance.
Using the prototype tool in a case study with real work order data, analytical effort for certain datasets was reduced by 90% - from two working weeks to a day. In addition, the active learning framework resulted in an approach that end users described as “practical” and “intuitive” during testing. An in-depth review was also conducted regarding the uncertainty of the results – a key factor for implementation in a decision-making context.
The outcomes of this work showcase the potential of machine learning to drive the digitization of not only new installations, but also older assets, where as a result the large amount of unstructured historical data becomes an advantage rather than a hindrance. User testing results encourage a wider uptake of machine learning solutions in the industry, and particularly a shift towards more accessible in-house analytical capabilities.

Conference

ConferenceSPE Offshore Europe 2019
CountryUnited Kingdom
CityAberdeen
Period3/09/196/09/19
Internet address

Fingerprint

Analog to digital conversion
Learning systems
Testing
Industry
Decision making
Health
Problem-Based Learning

Keywords

  • text mining
  • digitisation
  • data processing
  • maintenance assessement

Cite this

Salo, E., McMillan, D., & Connor, R. (Accepted/In press). Work orders - value from structureless text in the era of digitisation. Paper presented at SPE Offshore Europe 2019, Aberdeen, United Kingdom.
Salo, Erik ; McMillan, David ; Connor, Richard. / Work orders - value from structureless text in the era of digitisation. Paper presented at SPE Offshore Europe 2019, Aberdeen, United Kingdom.
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Salo, E, McMillan, D & Connor, R 2019, 'Work orders - value from structureless text in the era of digitisation' Paper presented at SPE Offshore Europe 2019, Aberdeen, United Kingdom, 3/09/19 - 6/09/19, .

Work orders - value from structureless text in the era of digitisation. / Salo, Erik; McMillan, David; Connor, Richard.

2019. Paper presented at SPE Offshore Europe 2019, Aberdeen, United Kingdom.

Research output: Contribution to conferencePaper

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T1 - Work orders - value from structureless text in the era of digitisation

AU - Salo, Erik

AU - McMillan, David

AU - Connor, Richard

PY - 2019/5/31

Y1 - 2019/5/31

N2 - Free text and hand-written reports are losing ground to digitization fast, however many hours of effort are still lost across the industry to the manual creation and analysis of these data types. Work orders in particular contain valuable information from failure rates to asset health, but at the same time present operators with such analytical difficulties and lack of structure that many are missing out on the value completely. This research challenges the current mainstream practice of manual work order analysis by presenting a methodology fit for today’s context of efficiency and digitization.A prototype text mining software for work order analysis was developed and tested in a user-oriented approach in cooperation with industrial partners. The final prototype combines classical machine learning methods, such as hierarchical clustering, with the operator’s expert knowledge obtained via an active learning approach. A novel distance metric in this context was adapted from information-theoretical research to improve clustering performance.Using the prototype tool in a case study with real work order data, analytical effort for certain datasets was reduced by 90% - from two working weeks to a day. In addition, the active learning framework resulted in an approach that end users described as “practical” and “intuitive” during testing. An in-depth review was also conducted regarding the uncertainty of the results – a key factor for implementation in a decision-making context.The outcomes of this work showcase the potential of machine learning to drive the digitization of not only new installations, but also older assets, where as a result the large amount of unstructured historical data becomes an advantage rather than a hindrance. User testing results encourage a wider uptake of machine learning solutions in the industry, and particularly a shift towards more accessible in-house analytical capabilities.

AB - Free text and hand-written reports are losing ground to digitization fast, however many hours of effort are still lost across the industry to the manual creation and analysis of these data types. Work orders in particular contain valuable information from failure rates to asset health, but at the same time present operators with such analytical difficulties and lack of structure that many are missing out on the value completely. This research challenges the current mainstream practice of manual work order analysis by presenting a methodology fit for today’s context of efficiency and digitization.A prototype text mining software for work order analysis was developed and tested in a user-oriented approach in cooperation with industrial partners. The final prototype combines classical machine learning methods, such as hierarchical clustering, with the operator’s expert knowledge obtained via an active learning approach. A novel distance metric in this context was adapted from information-theoretical research to improve clustering performance.Using the prototype tool in a case study with real work order data, analytical effort for certain datasets was reduced by 90% - from two working weeks to a day. In addition, the active learning framework resulted in an approach that end users described as “practical” and “intuitive” during testing. An in-depth review was also conducted regarding the uncertainty of the results – a key factor for implementation in a decision-making context.The outcomes of this work showcase the potential of machine learning to drive the digitization of not only new installations, but also older assets, where as a result the large amount of unstructured historical data becomes an advantage rather than a hindrance. User testing results encourage a wider uptake of machine learning solutions in the industry, and particularly a shift towards more accessible in-house analytical capabilities.

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Salo E, McMillan D, Connor R. Work orders - value from structureless text in the era of digitisation. 2019. Paper presented at SPE Offshore Europe 2019, Aberdeen, United Kingdom.