A diagnostic knowledge model of wind turbine fault

Hongwei Wang; Wei  Liu; Zhanli Liu

A diagnostic knowledge model of wind turbine fault

Hongwei Wang, Wei Liu, Zhanli Liu

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

Abstract

With the development of the wind power industry, wind power has become one of the main green generation energy. At the same time, with the wind power installed capacity increasing, the failure rate gradually growth. As wind turbine is a complex electromechanical equipment, the fault diagnosis for this kind of equipment is also a complicated process. Focused on the current shortage of fault diagnosis knowledge representation, this paper proposes a diagnostic knowledge model for wind turbine and also elaborates the model structure definition with a target to ensure the accuracy of fault diagnosis. Besides, this model can also offer assistance reference model for researchers in related fields to develop advanced methods for sharing and reuse of diagnostic knowledge.

Original language	English
Title of host publication	Intelligent Robotics and Applications
Subtitle of host publication	10th International Conference, ICIRA 2017, Wuhan, China, August 16–18, 2017, Proceedings, Part III
Editors	Y. Huang, H. Wu, H. Liu, Z. Yin
Publisher	Springer
Pages	437-448
Number of pages	11
ISBN (Electronic)	978-3319652986
ISBN (Print)	978-3319652979
Publication status	Published - Sept 2017
Event	International Conference on Intelligent Robotics and Applications (ICIRA) - Wuhan, China Duration: 16 Aug 2017 → 18 Aug 2017

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10464
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Intelligent Robotics and Applications (ICIRA)
Country/Territory	China
City	Wuhan
Period	16/08/17 → 18/08/17

Keywords

wind turbine
fault diagnosis
knowledge model

Access to Document

https://link.springer.com/chapter/10.1007/978-3-319-65298-6_40

Cite this

Wang, H., Liu, W., & Liu, Z. (2017). A diagnostic knowledge model of wind turbine fault. In Y. Huang, H. Wu, H. Liu, & Z. Yin (Eds.), Intelligent Robotics and Applications: 10th International Conference, ICIRA 2017, Wuhan, China, August 16–18, 2017, Proceedings, Part III (pp. 437-448). (Lecture Notes in Computer Science; Vol. 10464). Springer. https://link.springer.com/chapter/10.1007/978-3-319-65298-6_40

@inproceedings{7c2fd82098ac44a5b71c34a3e6c18859,

title = "A diagnostic knowledge model of wind turbine fault",

abstract = "With the development of the wind power industry, wind power has become one of the main green generation energy. At the same time, with the wind power installed capacity increasing, the failure rate gradually growth. As wind turbine is a complex electromechanical equipment, the fault diagnosis for this kind of equipment is also a complicated process. Focused on the current shortage of fault diagnosis knowledge representation, this paper proposes a diagnostic knowledge model for wind turbine and also elaborates the model structure definition with a target to ensure the accuracy of fault diagnosis. Besides, this model can also offer assistance reference model for researchers in related fields to develop advanced methods for sharing and reuse of diagnostic knowledge. ",

keywords = "wind turbine, fault diagnosis, knowledge model",

author = "Hongwei Wang and Wei Liu and Zhanli Liu",

note = "DOI not working - 10.1007/978-3-319-65298-6_40; International Conference on Intelligent Robotics and Applications (ICIRA) ; Conference date: 16-08-2017 Through 18-08-2017",

year = "2017",

month = sep,

language = "English",

isbn = "978-3319652979",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "437--448",

editor = "Y. Huang and H. Wu and H. Liu and Z. Yin",

booktitle = "Intelligent Robotics and Applications",

}

Wang, H, Liu, W & Liu, Z 2017, A diagnostic knowledge model of wind turbine fault. in Y Huang, H Wu, H Liu & Z Yin (eds), Intelligent Robotics and Applications: 10th International Conference, ICIRA 2017, Wuhan, China, August 16–18, 2017, Proceedings, Part III. Lecture Notes in Computer Science, vol. 10464, Springer, pp. 437-448, International Conference on Intelligent Robotics and Applications (ICIRA), Wuhan, China, 16/08/17. <https://link.springer.com/chapter/10.1007/978-3-319-65298-6_40>

A diagnostic knowledge model of wind turbine fault. / Wang, Hongwei; Liu, Wei ; Liu, Zhanli.
Intelligent Robotics and Applications: 10th International Conference, ICIRA 2017, Wuhan, China, August 16–18, 2017, Proceedings, Part III. ed. / Y. Huang; H. Wu; H. Liu; Z. Yin. Springer, 2017. p. 437-448 (Lecture Notes in Computer Science; Vol. 10464).

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

TY - GEN

T1 - A diagnostic knowledge model of wind turbine fault

AU - Wang, Hongwei

AU - Liu, Wei

AU - Liu, Zhanli

N1 - DOI not working - 10.1007/978-3-319-65298-6_40

PY - 2017/9

Y1 - 2017/9

N2 - With the development of the wind power industry, wind power has become one of the main green generation energy. At the same time, with the wind power installed capacity increasing, the failure rate gradually growth. As wind turbine is a complex electromechanical equipment, the fault diagnosis for this kind of equipment is also a complicated process. Focused on the current shortage of fault diagnosis knowledge representation, this paper proposes a diagnostic knowledge model for wind turbine and also elaborates the model structure definition with a target to ensure the accuracy of fault diagnosis. Besides, this model can also offer assistance reference model for researchers in related fields to develop advanced methods for sharing and reuse of diagnostic knowledge.

AB - With the development of the wind power industry, wind power has become one of the main green generation energy. At the same time, with the wind power installed capacity increasing, the failure rate gradually growth. As wind turbine is a complex electromechanical equipment, the fault diagnosis for this kind of equipment is also a complicated process. Focused on the current shortage of fault diagnosis knowledge representation, this paper proposes a diagnostic knowledge model for wind turbine and also elaborates the model structure definition with a target to ensure the accuracy of fault diagnosis. Besides, this model can also offer assistance reference model for researchers in related fields to develop advanced methods for sharing and reuse of diagnostic knowledge.

KW - wind turbine

KW - fault diagnosis

KW - knowledge model

M3 - Conference contribution

SN - 978-3319652979

T3 - Lecture Notes in Computer Science

SP - 437

EP - 448

BT - Intelligent Robotics and Applications

A2 - Huang, Y.

A2 - Wu, H.

A2 - Liu, H.

A2 - Yin, Z.

PB - Springer

T2 - International Conference on Intelligent Robotics and Applications (ICIRA)

Y2 - 16 August 2017 through 18 August 2017

ER -

A diagnostic knowledge model of wind turbine fault

Abstract

Publication series

Conference

Keywords

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