An improved empirical wavelet transform study based on wind turbine condition monitoring signals

Pu Shi; Wenxian Yang; Paul McKeever; Wenye Tian; Hyunjoo Lee; Chong Ng

An improved empirical wavelet transform study based on wind turbine condition monitoring signals

Pu Shi, Wenxian Yang, Paul McKeever, Wenye Tian, Hyunjoo Lee, Chong Ng

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

Abstract

Reliable condition monitoring (CM) highly relies on the correctness of fault-related features extraction from CM signals. The conventional EWT adopts default method to pre-define values for both mode number and mode boundaries in spectrum. It is not adaptive to the signals being inspected. As a consequence, it would lead to inaccurate feature extraction thus unreliable WT CM result sometimes. For this reason, an improved EWT method is investigated in this paper to precisely extract features. The main contribution of this paper focuses on the development of data-driven adaptive spectrum segment method to perform improved EWT. The experiments have shown that thanks to the use of optimization algorithm, the fault-related features buried in WT CM signals have been extracted out successfully.

Original language	English
Title of host publication	Proceedings of the Thirteenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2016/MFPT 2016)
Publisher	British Institute of Non-Destructive Testing
Pages	296-307
Number of pages	12
ISBN (Print)	978090313263X, 9781510830936
Publication status	Published - 10 Oct 2016
Externally published	Yes
Event	13th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies - Paris, France Duration: 10 Oct 2016 → 12 Oct 2016 Conference number: 13

Conference

Conference	13th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies
Abbreviated title	CM & MFPT 2016
Country/Territory	France
City	Paris
Period	10/10/16 → 12/10/16

UN SDGs

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

SDG 7 Affordable and Clean Energy

Cite this

Shi, P., Yang, W., McKeever, P., Tian, W., Lee, H., & Ng, C. (2016). An improved empirical wavelet transform study based on wind turbine condition monitoring signals. In Proceedings of the Thirteenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2016/MFPT 2016) (pp. 296-307). British Institute of Non-Destructive Testing.

@inproceedings{3722f77acb734a869390cd4818faf18b,

title = "An improved empirical wavelet transform study based on wind turbine condition monitoring signals",

abstract = "Reliable condition monitoring (CM) highly relies on the correctness of fault-related features extraction from CM signals. The conventional EWT adopts default method to pre-define values for both mode number and mode boundaries in spectrum. It is not adaptive to the signals being inspected. As a consequence, it would lead to inaccurate feature extraction thus unreliable WT CM result sometimes. For this reason, an improved EWT method is investigated in this paper to precisely extract features. The main contribution of this paper focuses on the development of data-driven adaptive spectrum segment method to perform improved EWT. The experiments have shown that thanks to the use of optimization algorithm, the fault-related features buried in WT CM signals have been extracted out successfully.",

keywords = "Condition monitoring, Empirical wavelet transform, Fault diagnosis, Wind turbine",

author = "Pu Shi and Wenxian Yang and Paul McKeever and Wenye Tian and Hyunjoo Lee and Chong Ng",

note = "Publisher Copyright: {\textcopyright} 2016, British Institute of Non-Destructive Testing. All rights reserved.; 13th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM \& MFPT 2016 ; Conference date: 10-10-2016 Through 12-10-2016",

year = "2016",

month = oct,

day = "10",

language = "English",

isbn = "978090313263X",

pages = "296--307",

booktitle = "Proceedings of the Thirteenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2016/MFPT 2016)",

publisher = "British Institute of Non-Destructive Testing",

address = "United Kingdom",

}

Shi, P, Yang, W, McKeever, P, Tian, W, Lee, H & Ng, C 2016, An improved empirical wavelet transform study based on wind turbine condition monitoring signals. in Proceedings of the Thirteenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2016/MFPT 2016). British Institute of Non-Destructive Testing, pp. 296-307, 13th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Paris, France, 10/10/16.

An improved empirical wavelet transform study based on wind turbine condition monitoring signals. / Shi, Pu; Yang, Wenxian; McKeever, Paul et al.
Proceedings of the Thirteenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2016/MFPT 2016). British Institute of Non-Destructive Testing, 2016. p. 296-307.

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

TY - GEN

T1 - An improved empirical wavelet transform study based on wind turbine condition monitoring signals

AU - Shi, Pu

AU - Yang, Wenxian

AU - McKeever, Paul

AU - Tian, Wenye

AU - Lee, Hyunjoo

AU - Ng, Chong

N1 - Conference code: 13

PY - 2016/10/10

Y1 - 2016/10/10

N2 - Reliable condition monitoring (CM) highly relies on the correctness of fault-related features extraction from CM signals. The conventional EWT adopts default method to pre-define values for both mode number and mode boundaries in spectrum. It is not adaptive to the signals being inspected. As a consequence, it would lead to inaccurate feature extraction thus unreliable WT CM result sometimes. For this reason, an improved EWT method is investigated in this paper to precisely extract features. The main contribution of this paper focuses on the development of data-driven adaptive spectrum segment method to perform improved EWT. The experiments have shown that thanks to the use of optimization algorithm, the fault-related features buried in WT CM signals have been extracted out successfully.

AB - Reliable condition monitoring (CM) highly relies on the correctness of fault-related features extraction from CM signals. The conventional EWT adopts default method to pre-define values for both mode number and mode boundaries in spectrum. It is not adaptive to the signals being inspected. As a consequence, it would lead to inaccurate feature extraction thus unreliable WT CM result sometimes. For this reason, an improved EWT method is investigated in this paper to precisely extract features. The main contribution of this paper focuses on the development of data-driven adaptive spectrum segment method to perform improved EWT. The experiments have shown that thanks to the use of optimization algorithm, the fault-related features buried in WT CM signals have been extracted out successfully.

KW - Condition monitoring

KW - Empirical wavelet transform

KW - Fault diagnosis

KW - Wind turbine

UR - https://www.scopus.com/pages/publications/85003000400

UR - https://www.bindt.org/shopbindt/conference-proceedings/proceedings-of-cm-mfpt-2016.html

M3 - Conference contribution

AN - SCOPUS:85003000400

SN - 978090313263X

SN - 9781510830936

SP - 296

EP - 307

BT - Proceedings of the Thirteenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2016/MFPT 2016)

PB - British Institute of Non-Destructive Testing

T2 - 13th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies

Y2 - 10 October 2016 through 12 October 2016

ER -

An improved empirical wavelet transform study based on wind turbine condition monitoring signals

Abstract

Conference

UN SDGs

Other files and links

Fingerprint

Cite this