Fault diagnosis of wind turbine using local mean decomposition and synchrosqueezing transforms

Yanjie Guo; Xuefeng Chen; Shibin Wang; Xiang Li; Ruonan Liu

doi:10.1109/I2MTC.2016.7520584

Fault diagnosis of wind turbine using local mean decomposition and synchrosqueezing transforms

Yanjie Guo
, Xuefeng Chen
, Shibin Wang
, Xiang Li
, Ruonan Liu

School of Mechanical Engineering

Xi'an Jiaotong University

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

4 Scopus citations

Abstract

Wind turbine is driven by natural wind, thus the vibration signal of gearbox is influenced and performs with nature of non-stationary and noisy. In order to analyze the variable speed signal, time-frequency representation is introduced as a powerful tool. The synchrosqueezing transform is a useful time-frequency representation method in many situation but can not restrain noise in high noise background. And local mean decomposition can decompose noise from the signal and remain the character of signals. In this paper, we proposed a new method for fault diagnosis of wind turbine with help of synchrosqueezing transform and local mean decomposition, which can denoise and represent the frequency of signals changing with time. This method is validated by simulated signals and experimental field test. The results showed the proposed method can restrain the noise and decrease the inaccuracy of time-frequency representation.

Original language	English
Title of host publication	I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference
Subtitle of host publication	Measuring the Pulse of Industries, Nature and Humans, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467392204
DOIs	https://doi.org/10.1109/I2MTC.2016.7520584
State	Published - 22 Jul 2016
Event	2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016 - Taipei, Taiwan, Province of China Duration: 23 May 2016 → 26 May 2016

Publication series

Name	Conference Record - IEEE Instrumentation and Measurement Technology Conference
Volume	2016-July
ISSN (Print)	1091-5281

Conference

Conference	2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016
Country/Territory	Taiwan, Province of China
City	Taipei
Period	23/05/16 → 26/05/16

Keywords

SNR
local mean decomposition
synchrosqueezing transform
varibale speed signal
wind turbine gearbox vibration

Access to Document

10.1109/I2MTC.2016.7520584

Cite this

Guo, Y., Chen, X., Wang, S., Li, X., & Liu, R. (2016). Fault diagnosis of wind turbine using local mean decomposition and synchrosqueezing transforms. In I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings Article 7520584 (Conference Record - IEEE Instrumentation and Measurement Technology Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/I2MTC.2016.7520584

Guo, Yanjie ; Chen, Xuefeng ; Wang, Shibin et al. / Fault diagnosis of wind turbine using local mean decomposition and synchrosqueezing transforms. I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. (Conference Record - IEEE Instrumentation and Measurement Technology Conference).

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title = "Fault diagnosis of wind turbine using local mean decomposition and synchrosqueezing transforms",

abstract = "Wind turbine is driven by natural wind, thus the vibration signal of gearbox is influenced and performs with nature of non-stationary and noisy. In order to analyze the variable speed signal, time-frequency representation is introduced as a powerful tool. The synchrosqueezing transform is a useful time-frequency representation method in many situation but can not restrain noise in high noise background. And local mean decomposition can decompose noise from the signal and remain the character of signals. In this paper, we proposed a new method for fault diagnosis of wind turbine with help of synchrosqueezing transform and local mean decomposition, which can denoise and represent the frequency of signals changing with time. This method is validated by simulated signals and experimental field test. The results showed the proposed method can restrain the noise and decrease the inaccuracy of time-frequency representation.",

keywords = "SNR, local mean decomposition, synchrosqueezing transform, varibale speed signal, wind turbine gearbox vibration",

author = "Yanjie Guo and Xuefeng Chen and Shibin Wang and Xiang Li and Ruonan Liu",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016 ; Conference date: 23-05-2016 Through 26-05-2016",

year = "2016",

month = jul,

day = "22",

doi = "10.1109/I2MTC.2016.7520584",

language = "英语",

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Guo, Y, Chen, X , Wang, S, Li, X & Liu, R 2016, Fault diagnosis of wind turbine using local mean decomposition and synchrosqueezing transforms. in I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings., 7520584, Conference Record - IEEE Instrumentation and Measurement Technology Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016, Taipei, Taiwan, Province of China, 23/05/16. https://doi.org/10.1109/I2MTC.2016.7520584

Fault diagnosis of wind turbine using local mean decomposition and synchrosqueezing transforms. / Guo, Yanjie; Chen, Xuefeng ; Wang, Shibin et al.
I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7520584 (Conference Record - IEEE Instrumentation and Measurement Technology Conference; Vol. 2016-July).

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

TY - GEN

T1 - Fault diagnosis of wind turbine using local mean decomposition and synchrosqueezing transforms

AU - Guo, Yanjie

AU - Chen, Xuefeng

AU - Wang, Shibin

AU - Li, Xiang

AU - Liu, Ruonan

PY - 2016/7/22

Y1 - 2016/7/22

N2 - Wind turbine is driven by natural wind, thus the vibration signal of gearbox is influenced and performs with nature of non-stationary and noisy. In order to analyze the variable speed signal, time-frequency representation is introduced as a powerful tool. The synchrosqueezing transform is a useful time-frequency representation method in many situation but can not restrain noise in high noise background. And local mean decomposition can decompose noise from the signal and remain the character of signals. In this paper, we proposed a new method for fault diagnosis of wind turbine with help of synchrosqueezing transform and local mean decomposition, which can denoise and represent the frequency of signals changing with time. This method is validated by simulated signals and experimental field test. The results showed the proposed method can restrain the noise and decrease the inaccuracy of time-frequency representation.

AB - Wind turbine is driven by natural wind, thus the vibration signal of gearbox is influenced and performs with nature of non-stationary and noisy. In order to analyze the variable speed signal, time-frequency representation is introduced as a powerful tool. The synchrosqueezing transform is a useful time-frequency representation method in many situation but can not restrain noise in high noise background. And local mean decomposition can decompose noise from the signal and remain the character of signals. In this paper, we proposed a new method for fault diagnosis of wind turbine with help of synchrosqueezing transform and local mean decomposition, which can denoise and represent the frequency of signals changing with time. This method is validated by simulated signals and experimental field test. The results showed the proposed method can restrain the noise and decrease the inaccuracy of time-frequency representation.

KW - SNR

KW - local mean decomposition

KW - synchrosqueezing transform

KW - varibale speed signal

KW - wind turbine gearbox vibration

UR - https://www.scopus.com/pages/publications/84980325755

U2 - 10.1109/I2MTC.2016.7520584

DO - 10.1109/I2MTC.2016.7520584

M3 - 会议稿件

AN - SCOPUS:84980325755

T3 - Conference Record - IEEE Instrumentation and Measurement Technology Conference

BT - I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016

Y2 - 23 May 2016 through 26 May 2016

ER -

Guo Y, Chen X , Wang S, Li X, Liu R. Fault diagnosis of wind turbine using local mean decomposition and synchrosqueezing transforms. In I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. 7520584. (Conference Record - IEEE Instrumentation and Measurement Technology Conference). doi: 10.1109/I2MTC.2016.7520584

Fault diagnosis of wind turbine using local mean decomposition and synchrosqueezing transforms

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