Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey

Paul A. Adedeji; Obafemi O. Olatunji; Nkosinathi Madushele; Tien Chien Jen

doi:10.1109/ICMIMT52186.2021.9476200

Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

17 Citations (Scopus)

Abstract

Optimality of model hyperparameters is essential for intelligent condition monitoring (ICM) of wind turbines using machine learning models, hence the need for hyperparameter tuning. Evolutionary algorithms (EAs) have been used for hyperparameter tuning of machine learning models, however, little is known about the hyperparameter tuning of these EAs. This study presents a survey of hyperparameter tuning of EAs used for tuning hyperparameters of machine learning models that are used in ICM of wind turbines. Findings show that many studies tune hyperparameters for machine learning models, however, a few studies tune these hyperparameters with EAs. Among these few, a handful tune the hyperparameters of such EAs and such studies in ICM of wind turbines is very sparse. Hence the need to explore this double stage hyperparameter (DSHP) tuning in ICM of wind turbines.

Original language	English
Title of host publication	Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	254-258
Number of pages	5
ISBN (Electronic)	9780738144627
DOIs	https://doi.org/10.1109/ICMIMT52186.2021.9476200
Publication status	Published - 13 May 2021
Event	12th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021 - Cape Town, South Africa Duration: 13 May 2021 → 15 May 2021

Publication series

Name	Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021

Conference

Conference	12th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021
Country/Territory	South Africa
City	Cape Town
Period	13/05/21 → 15/05/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Condition monitoring
Evolutionary algorithm
Hyperparameters
Machine learning
Wind turbine

ASJC Scopus subject areas

Industrial and Manufacturing Engineering
Mechanical Engineering
Mechanics of Materials
Metals and Alloys
Artificial Intelligence
Civil and Structural Engineering

Access to Document

10.1109/ICMIMT52186.2021.9476200

Cite this

Adedeji, P. A., Olatunji, O. O., Madushele, N., & Jen, T. C. (2021). Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey. In Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021 (pp. 254-258). Article 9476200 (Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMIMT52186.2021.9476200

Adedeji, Paul A. ; Olatunji, Obafemi O. ; Madushele, Nkosinathi et al. / Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey. Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 254-258 (Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021).

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title = "Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey",

abstract = "Optimality of model hyperparameters is essential for intelligent condition monitoring (ICM) of wind turbines using machine learning models, hence the need for hyperparameter tuning. Evolutionary algorithms (EAs) have been used for hyperparameter tuning of machine learning models, however, little is known about the hyperparameter tuning of these EAs. This study presents a survey of hyperparameter tuning of EAs used for tuning hyperparameters of machine learning models that are used in ICM of wind turbines. Findings show that many studies tune hyperparameters for machine learning models, however, a few studies tune these hyperparameters with EAs. Among these few, a handful tune the hyperparameters of such EAs and such studies in ICM of wind turbines is very sparse. Hence the need to explore this double stage hyperparameter (DSHP) tuning in ICM of wind turbines.",

keywords = "Condition monitoring, Evolutionary algorithm, Hyperparameters, Machine learning, Wind turbine",

author = "Adedeji, \{Paul A.\} and Olatunji, \{Obafemi O.\} and Nkosinathi Madushele and Jen, \{Tien Chien\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 12th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021 ; Conference date: 13-05-2021 Through 15-05-2021",

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Adedeji, PA, Olatunji, OO, Madushele, N & Jen, TC 2021, Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey. in Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021., 9476200, Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021, Institute of Electrical and Electronics Engineers Inc., pp. 254-258, 12th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021, Cape Town, South Africa, 13/05/21. https://doi.org/10.1109/ICMIMT52186.2021.9476200

Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey. / Adedeji, Paul A.; Olatunji, Obafemi O.; Madushele, Nkosinathi et al.
Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 254-258 9476200 (Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey

AU - Adedeji, Paul A.

AU - Olatunji, Obafemi O.

AU - Madushele, Nkosinathi

AU - Jen, Tien Chien

PY - 2021/5/13

Y1 - 2021/5/13

N2 - Optimality of model hyperparameters is essential for intelligent condition monitoring (ICM) of wind turbines using machine learning models, hence the need for hyperparameter tuning. Evolutionary algorithms (EAs) have been used for hyperparameter tuning of machine learning models, however, little is known about the hyperparameter tuning of these EAs. This study presents a survey of hyperparameter tuning of EAs used for tuning hyperparameters of machine learning models that are used in ICM of wind turbines. Findings show that many studies tune hyperparameters for machine learning models, however, a few studies tune these hyperparameters with EAs. Among these few, a handful tune the hyperparameters of such EAs and such studies in ICM of wind turbines is very sparse. Hence the need to explore this double stage hyperparameter (DSHP) tuning in ICM of wind turbines.

AB - Optimality of model hyperparameters is essential for intelligent condition monitoring (ICM) of wind turbines using machine learning models, hence the need for hyperparameter tuning. Evolutionary algorithms (EAs) have been used for hyperparameter tuning of machine learning models, however, little is known about the hyperparameter tuning of these EAs. This study presents a survey of hyperparameter tuning of EAs used for tuning hyperparameters of machine learning models that are used in ICM of wind turbines. Findings show that many studies tune hyperparameters for machine learning models, however, a few studies tune these hyperparameters with EAs. Among these few, a handful tune the hyperparameters of such EAs and such studies in ICM of wind turbines is very sparse. Hence the need to explore this double stage hyperparameter (DSHP) tuning in ICM of wind turbines.

KW - Condition monitoring

KW - Evolutionary algorithm

KW - Hyperparameters

KW - Machine learning

KW - Wind turbine

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DO - 10.1109/ICMIMT52186.2021.9476200

M3 - Conference contribution

AN - SCOPUS:85111023130

T3 - Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021

SP - 254

EP - 258

BT - Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 13 May 2021 through 15 May 2021

ER -

Adedeji PA, Olatunji OO, Madushele N , Jen TC. Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey. In Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 254-258. 9476200. (Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021). doi: 10.1109/ICMIMT52186.2021.9476200

Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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