IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v28y2017i4d10.1007_s10845-015-1045-5.html
   My bibliography  Save this article

Machinery condition prediction based on wavelet and support vector machine

Author

Listed:
  • Shujie Liu

    (Dalian University of Technology)

  • Yawei Hu

    (Dalian University of Technology)

  • Chao Li

    (Dalian University of Technology
    Offshore Oil Engineering co., Ltd)

  • Huitian Lu

    (South Dakota State University)

  • Hongchao Zhang

    (Texas Tech University)

Abstract

The soft failure of mechanical equipment makes its performance drop gradually, which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. In this paper, the support vector machine (SVM), a novel learning machine based on the VC dimension theory of statistical learning theory, is described and applied in machinery condition prediction. To improve the modeling capability, wavelet transform (WT) is introduced into the SVM model to reduce the influence of irregular characteristics and simultaneously simplify the complexity of the original signal. The paper models the vibration signal from the double row bearing and wavelet transformation and SVM model (WT–SVM model) is constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships is applied to describe the degradation trend distribution and a 95 % confidence level based on $$t$$ t -distribution is given. The single SVM model and neural network (NN) approach is also investigated as a comparison. The modeling results indicate that the WT–SVM model outperforms the NN and single SVM models, and is feasible and effective in machinery condition prediction.

Suggested Citation

  • Shujie Liu & Yawei Hu & Chao Li & Huitian Lu & Hongchao Zhang, 2017. "Machinery condition prediction based on wavelet and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 1045-1055, April.
  • Handle: RePEc:spr:joinma:v:28:y:2017:i:4:d:10.1007_s10845-015-1045-5
    DOI: 10.1007/s10845-015-1045-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-015-1045-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-015-1045-5?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.

    Citations

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


    Cited by:

    1. Lei Fu & Yanding Wei & Sheng Fang & Xiaojun Zhou & Junqiang Lou, 2017. "Condition Monitoring for Roller Bearings of Wind Turbines Based on Health Evaluation under Variable Operating States," Energies, MDPI, vol. 10(10), pages 1-21, October.
    2. Yun Bai & Zhenzhong Sun & Bo Zeng & Jianyu Long & Lin Li & José Valente Oliveira & Chuan Li, 2019. "A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2245-2256, June.
    3. Andhi Indira Kusuma & Yi-Mei Huang, 2023. "Product quality prediction in pulsed laser cutting of silicon steel sheet using vibration signals and deep neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1683-1699, April.
    4. Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, January.
    5. Anshuman Kumar Sahu & Siba Sankar Mahapatra, 2021. "Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2125-2145, December.
    6. Deepam Goyal & Anurag Choudhary & B. S. Pabla & S. S. Dhami, 2020. "Support vector machines based non-contact fault diagnosis system for bearings," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1275-1289, June.
    7. Wei Qin & Dongye Zha & Jie Zhang, 2020. "An effective approach for causal variables analysis in diesel engine production by using mutual information and network deconvolution," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1661-1671, October.

    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:spr:joinma:v:28:y:2017:i:4:d:10.1007_s10845-015-1045-5. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.