Analysis of SAP work order data by turbine technology type for onshore wind

Research output: ThesisMaster's Thesis

Abstract

This thesis is a contribution to the field of wind turbine maintenance management. The first chapters provide a review of wind turbine maintenance management, in particular the motivations for reliability-centred maintenance. The data requirement of this maintenance approach is considered, and the role of work order free text data as an information source are highlighted. Methods from the field of text mining are however required to extract this information in an actionable format, and an overview of the most relevant text mining approaches is given in the final chapter of the literature review.
The main output of this work is a supervised text mining algorithm for structuring maintenance data that is recorded as free text work orders. The method is applied on two datasets of SAP work orders from major onshore wind farms in Scotland. Common issues found in the raw data are highlighted and data cleaning rule sets are developed to overcome these issues. A lexicon of domain terminology is developed that can be used on these datasets as well as extender for wider use. The methodology is developed in Matlab and consists of nine modules for data cleaning, vectorisation, transformations, supervised prediction of missing values. The outputs are given both as a two-level Pareto chart and frequency tables that allows their use in maintenance decision-making. Results are analysed in terms of algorithm performance and validated against the research aims.
Improvements are also suggested to reduce supervision requirement, raise accuracy, and make the approach more universal in terms of turbine models and terminology. Finally, the economic benefits of automated work order mining, and potential ways to increase its industrial appeal are discussed.
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
Supervisors/Advisors
  • McMillan, David, Supervisor
  • Tuohy, Paul Gerard, Supervisor
Award date7 Nov 2017
Place of PublicationGlasgow
Publisher
Publication statusPublished - 25 Aug 2017

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Turbines
Terminology
Wind turbines
Cleaning
Onshore wind farms
Decision making
Economics

Keywords

  • onshore wind
  • O&M
  • maintenance cost

Cite this

@phdthesis{cbf9cc53e616461aa59e53bc71a33078,
title = "Analysis of SAP work order data by turbine technology type for onshore wind",
abstract = "This thesis is a contribution to the field of wind turbine maintenance management. The first chapters provide a review of wind turbine maintenance management, in particular the motivations for reliability-centred maintenance. The data requirement of this maintenance approach is considered, and the role of work order free text data as an information source are highlighted. Methods from the field of text mining are however required to extract this information in an actionable format, and an overview of the most relevant text mining approaches is given in the final chapter of the literature review.The main output of this work is a supervised text mining algorithm for structuring maintenance data that is recorded as free text work orders. The method is applied on two datasets of SAP work orders from major onshore wind farms in Scotland. Common issues found in the raw data are highlighted and data cleaning rule sets are developed to overcome these issues. A lexicon of domain terminology is developed that can be used on these datasets as well as extender for wider use. The methodology is developed in Matlab and consists of nine modules for data cleaning, vectorisation, transformations, supervised prediction of missing values. The outputs are given both as a two-level Pareto chart and frequency tables that allows their use in maintenance decision-making. Results are analysed in terms of algorithm performance and validated against the research aims.Improvements are also suggested to reduce supervision requirement, raise accuracy, and make the approach more universal in terms of turbine models and terminology. Finally, the economic benefits of automated work order mining, and potential ways to increase its industrial appeal are discussed.",
keywords = "onshore wind, O&M, maintenance cost",
author = "Erik Salo",
year = "2017",
month = "8",
day = "25",
language = "English",
publisher = "University of Strathclyde",
school = "University Of Strathclyde",

}

Analysis of SAP work order data by turbine technology type for onshore wind. / Salo, Erik.

Glasgow : University of Strathclyde, 2017. 70 p.

Research output: ThesisMaster's Thesis

TY - THES

T1 - Analysis of SAP work order data by turbine technology type for onshore wind

AU - Salo, Erik

PY - 2017/8/25

Y1 - 2017/8/25

N2 - This thesis is a contribution to the field of wind turbine maintenance management. The first chapters provide a review of wind turbine maintenance management, in particular the motivations for reliability-centred maintenance. The data requirement of this maintenance approach is considered, and the role of work order free text data as an information source are highlighted. Methods from the field of text mining are however required to extract this information in an actionable format, and an overview of the most relevant text mining approaches is given in the final chapter of the literature review.The main output of this work is a supervised text mining algorithm for structuring maintenance data that is recorded as free text work orders. The method is applied on two datasets of SAP work orders from major onshore wind farms in Scotland. Common issues found in the raw data are highlighted and data cleaning rule sets are developed to overcome these issues. A lexicon of domain terminology is developed that can be used on these datasets as well as extender for wider use. The methodology is developed in Matlab and consists of nine modules for data cleaning, vectorisation, transformations, supervised prediction of missing values. The outputs are given both as a two-level Pareto chart and frequency tables that allows their use in maintenance decision-making. Results are analysed in terms of algorithm performance and validated against the research aims.Improvements are also suggested to reduce supervision requirement, raise accuracy, and make the approach more universal in terms of turbine models and terminology. Finally, the economic benefits of automated work order mining, and potential ways to increase its industrial appeal are discussed.

AB - This thesis is a contribution to the field of wind turbine maintenance management. The first chapters provide a review of wind turbine maintenance management, in particular the motivations for reliability-centred maintenance. The data requirement of this maintenance approach is considered, and the role of work order free text data as an information source are highlighted. Methods from the field of text mining are however required to extract this information in an actionable format, and an overview of the most relevant text mining approaches is given in the final chapter of the literature review.The main output of this work is a supervised text mining algorithm for structuring maintenance data that is recorded as free text work orders. The method is applied on two datasets of SAP work orders from major onshore wind farms in Scotland. Common issues found in the raw data are highlighted and data cleaning rule sets are developed to overcome these issues. A lexicon of domain terminology is developed that can be used on these datasets as well as extender for wider use. The methodology is developed in Matlab and consists of nine modules for data cleaning, vectorisation, transformations, supervised prediction of missing values. The outputs are given both as a two-level Pareto chart and frequency tables that allows their use in maintenance decision-making. Results are analysed in terms of algorithm performance and validated against the research aims.Improvements are also suggested to reduce supervision requirement, raise accuracy, and make the approach more universal in terms of turbine models and terminology. Finally, the economic benefits of automated work order mining, and potential ways to increase its industrial appeal are discussed.

KW - onshore wind

KW - O&M

KW - maintenance cost

M3 - Master's Thesis

PB - University of Strathclyde

CY - Glasgow

ER -