IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v161y2020icp736-749.html
   My bibliography  Save this article

Reassigned second-order Synchrosqueezing Transform and its application to wind turbine fault diagnosis

Author

Listed:
  • Yi, Cancan
  • Yu, Zhaohong
  • Lv, Yong
  • Xiao, Han

Abstract

Due to constant change of rotational speed and low frequency property of vibration signal, it is difficult to identify the weak time-varying fault features of direct-driven wind turbines during early period. In view of the problems of variable speed and strong time-varying characteristics, the traditional Time-Frequency (TF) analysis methods always generate a low TF resolution. Rearrangement Method (RM), as a mathematically excellent and efficient solution, is used to improve the TF energy localization, whereas it cannot reconstruct signals. To make TF plane more readable, RM can be utilized to make up the deficiency of the frequency-reassigned Synchrosqueezing Transform (SST). On such basis, this paper puts forward a novel TF analysis method, namely RFSST2, which has drawn on the advantages of RM and Second-Order Synchrosqueezing Transform based on STFT (FSST2). This method is used to sharpen the blurry TF energy for strong time-varying modulated signals. Besides, it can reconstruct the perfect signals. After the extraction the rotation speed curve from high-resolution TF plane generated by RFSST2, the order analysis without tachometer is performed to address the problem of variably rotating speed. To prove the proposed method is superior in processing complex multi-component signals, we respectively estimate the variable speed of wind turbines and deal with the fault signals from the gear fault simulation test-bed and from actual 1.5 MW direct-drive wind turbines. The analysis result obtained by comparing different TF methods shows that the method put forward by this paper can effectively extract time-varying fault characteristics under non-stationary condition.

Suggested Citation

  • Yi, Cancan & Yu, Zhaohong & Lv, Yong & Xiao, Han, 2020. "Reassigned second-order Synchrosqueezing Transform and its application to wind turbine fault diagnosis," Renewable Energy, Elsevier, vol. 161(C), pages 736-749.
    • Handle: RePEc:eee:renene:v:161:y:2020:i:c:p:736-749
    DOI: 10.1016/j.renene.2020.07.128
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148120312040
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2020.07.128?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Feng, Zhipeng & Liang, Ming & Zhang, Yi & Hou, Shumin, 2012. "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation," Renewable Energy, Elsevier, vol. 47(C), pages 112-126.
    2. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    3. Elforjani, Mohamed, 2020. "Diagnosis and prognosis of real world wind turbine gears," Renewable Energy, Elsevier, vol. 147(P1), pages 1676-1693.
    4. Teng, Wei & Ding, Xian & Cheng, Hao & Han, Chen & Liu, Yibing & Mu, Haihua, 2019. "Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform," Renewable Energy, Elsevier, vol. 136(C), pages 393-402.
    5. Tang, Baoping & Liu, Wenyi & Song, Tao, 2010. "Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution," Renewable Energy, Elsevier, vol. 35(12), pages 2862-2866.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
    2. Liu, Dongdong & Cui, Lingli & Cheng, Weidong, 2023. "Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation," Renewable Energy, Elsevier, vol. 206(C), pages 645-657.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    2. Wu, Zhe & Zhang, Qiang & Cheng, Lifeng & Hou, Shuyong & Tan, Shengyue, 2020. "The VMTES: Application to the structural health monitoring and diagnosis of rotating machines," Renewable Energy, Elsevier, vol. 162(C), pages 2380-2396.
    3. Kong, Yun & Wang, Tianyang & Feng, Zhipeng & Chu, Fulei, 2020. "Discriminative dictionary learning based sparse representation classification for intelligent fault identification of planet bearings in wind turbine," Renewable Energy, Elsevier, vol. 152(C), pages 754-769.
    4. Zhang, Yu & Lu, Wenxiu & Chu, Fulei, 2017. "Planet gear fault localization for wind turbine gearbox using acoustic emission signals," Renewable Energy, Elsevier, vol. 109(C), pages 449-460.
    5. Guo, Sheng & Yang, Tao & Hua, Haochen & Cao, Junwei, 2021. "Coupling fault diagnosis of wind turbine gearbox based on multitask parallel convolutional neural networks with overall information," Renewable Energy, Elsevier, vol. 178(C), pages 639-650.
    6. He, Deqiang & Liu, Chenyu & Jin, Zhenzhen & Ma, Rui & Chen, Yanjun & Shan, Sheng, 2022. "Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning," Energy, Elsevier, vol. 239(PB).
    7. Xueli An & Dongxiang Jiang, 2014. "Bearing fault diagnosis of wind turbine based on intrinsic time-scale decomposition frequency spectrum," Journal of Risk and Reliability, , vol. 228(6), pages 558-566, December.
    8. Kong, Yun & Wang, Tianyang & Chu, Fulei, 2019. "Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear," Renewable Energy, Elsevier, vol. 132(C), pages 1373-1388.
    9. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    10. Li, Jimeng & Chen, Xuefeng & Du, Zhaohui & Fang, Zuowei & He, Zhengjia, 2013. "A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis," Renewable Energy, Elsevier, vol. 60(C), pages 7-19.
    11. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
    12. Merainani, Boualem & Laddada, Sofiane & Bechhoefer, Eric & Chikh, Mohamed Abdessamed Ait & Benazzouz, Djamel, 2022. "An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples," Renewable Energy, Elsevier, vol. 182(C), pages 1141-1151.
    13. Liu, Wenyi, 2016. "Design and kinetic analysis of wind turbine blade-hub-tower coupled system," Renewable Energy, Elsevier, vol. 94(C), pages 547-557.
    14. Shujie Yang & Peikun Yang & Hao Yu & Jing Bai & Wuwei Feng & Yuxiang Su & Yulin Si, 2022. "A 2DCNN-RF Model for Offshore Wind Turbine High-Speed Bearing-Fault Diagnosis under Noisy Environment," Energies, MDPI, vol. 15(9), pages 1-16, May.
    15. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    16. Wenyi, Liu & Zhenfeng, Wang & Jiguang, Han & Guangfeng, Wang, 2013. "Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM," Renewable Energy, Elsevier, vol. 50(C), pages 1-6.
    17. Tengda Huang & Sheng Fu & Haonan Feng & Jiafeng Kuang, 2019. "Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention," Energies, MDPI, vol. 12(20), pages 1-19, October.
    18. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.
    19. Kandukuri, Surya Teja & Klausen, Andreas & Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar, 2016. "A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 697-708.
    20. Liu, Xianzeng & Yang, Yuhu & Zhang, Jun, 2018. "Resultant vibration signal model based fault diagnosis of a single stage planetary gear train with an incipient tooth crack on the sun gear," Renewable Energy, Elsevier, vol. 122(C), pages 65-79.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:161:y:2020:i:c:p:736-749. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.