IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v233y2019i2p175-185.html
   My bibliography  Save this article

Operation reliability evaluation of cutting tools based on singular value decomposition transform and support vector space

Author

Listed:
  • Baojia Chen
  • Baoming Shen
  • Fajun Zhang
  • Wenrong Xiao
  • Fafa Chen
  • Hongliang Tian
  • Shu Chen

Abstract

The traditional reliability evaluation method based on large sample statistics is inefficient for a single or a small batch computer numerical control turning cutting tool due to the inadequate description of time, dynamic process, inaccurate model and individualization. To solve the problem, a new operation reliability evaluation method based on singular value decomposition transform and support vector space is proposed. In this new method, the singular value decomposition is used for the dimensionality reduction of high-dimensional feature data so as to reduce the computational complexity and the redundant components. The hypersphere space of the similar data is established based on the dimension reduction data. The relative distance between the sample points and the hypersphere is then calculated and used to describe the performance of the tool. The semi-normal function is introduced to define the mapping relationship of the relative distance and the operation reliability of the tool. Finally, two cutting tools in the experiment are taken as the research example to verify the effectiveness of the method. The result shows that this method can evaluate the operation reliability of the tool effectively and the singular value decomposition dimensionality reduction improves the accuracy of the evaluation. It provides a new theoretical and practical support for the reliability evaluation of small sample data.

Suggested Citation

  • Baojia Chen & Baoming Shen & Fajun Zhang & Wenrong Xiao & Fafa Chen & Hongliang Tian & Shu Chen, 2019. "Operation reliability evaluation of cutting tools based on singular value decomposition transform and support vector space," Journal of Risk and Reliability, , vol. 233(2), pages 175-185, April.
    • Handle: RePEc:sae:risrel:v:233:y:2019:i:2:p:175-185
    DOI: 10.1177/1748006X18766125
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X18766125
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X18766125?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
    ---><---

    References listed on IDEAS

    as
    1. Peng, Weiwen & Li, Yan-Feng & Mi, Jinhua & Yu, Le & Huang, Hong-Zhong, 2016. "Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 75-87.
    2. Fan, Mengfei & Zeng, Zhiguo & Zio, Enrico & Kang, Rui, 2017. "Modeling dependent competing failure processes with degradation-shock dependence," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 422-430.
    Full references (including those not matched with items on IDEAS)

    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. Zhai, Qingqing & Chen, Piao & Hong, Lanqing & Shen, Lijuan, 2018. "A random-effects Wiener degradation model based on accelerated failure time," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 94-103.
    2. Mai, Yuxi & Xue, Jianwu & Wu, Bei, 2023. "Optimal maintenance policy for systems with environment-modulated degradation and random shocks considering imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    3. Chang, Miaoxin & Huang, Xianzhen & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2021. "Reliability analysis for systems based on degradation rates and hard failure thresholds changing with degradation levels," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Zeng, Zhiguo & Fang, Yi-Ping & Zhai, Qingqing & Du, Shijia, 2021. "A Markov reward process-based framework for resilience analysis of multistate energy systems under the threat of extreme events," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    5. Chi, Zhexiang & Chen, Ruoran & Huang, Simin & Li, Yan-Fu & Zhou, Bin & Zhang, Wenjuan, 2020. "Multi-State System Modeling and Reliability Assessment for Groups of High-speed Train Wheels," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    6. Fang, Jiayue & Kang, Rui & Chen, Ying, 2021. "Reliability evaluation of non-repairable systems with failure mechanism trigger effect," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    7. Liu, Di & Wang, Shaoping, 2021. "An artificial neural network supported stochastic process for degradation modeling and prediction," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    9. Zhao, Yixin & Cai, Baoping & Cozzani, Valerio & Liu, Yiliu, 2025. "Failure dependence and cascading failures: A literature review and research opportunities," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    10. Song, Wanqing & Duan, Shouwu & Zio, Enrico & Kudreyko, Aleksey, 2022. "Multifractional and long-range dependent characteristics for remaining useful life prediction of cracking gas compressor," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. Liu, Yao & Wang, Yashun & Fan, Zhengwei & Bai, Guanghan & Chen, Xun, 2021. "Reliability modeling and a statistical inference method of accelerated degradation testing with multiple stresses and dependent competing failure processes," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    12. Linmin Hu & Rui Peng, 2019. "Reliability modeling for a discrete time multi-state system with random and dependent transition probabilities," Journal of Risk and Reliability, , vol. 233(5), pages 747-760, October.
    13. Minhee Kim & Todd Allen & Kaibo Liu, 2023. "Covariate Dependent Sparse Functional Data Analysis," INFORMS Joural on Data Science, INFORMS, vol. 2(1), pages 81-98, April.
    14. Feng, Tingting & Li, Shichao & Guo, Liang & Gao, Hongli & Chen, Tao & Yu, Yaoxiang, 2023. "A degradation-shock dependent competing failure processes based method for remaining useful life prediction of drill bit considering time-shifting sudden failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    15. Zhang, Yamei & Zhao, Songzheng & Wu, Bei, 2025. "Reliability and maintenance analysis of two-component system subject to zoned shocks and degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 257(PA).
    16. Liu, Di & Wang, Shaoping, 2020. "A degradation modeling and reliability estimation method based on Wiener process and evidential variable," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    17. Fang, Chen & Zeng, Chenhao & Li, Jiaran & Chen, Jianhui, 2025. "Reliability analysis of consecutive -ks-outs-of-ns: F system with a circular polygon structure considering subsystems balance in shock environment," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    18. Chen, Yunxia & Zhang, Wenbo & Xu, Dan, 2019. "Reliability assessment with varying safety threshold for shock resistant systems," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 49-60.
    19. Zeng, Zhiguo & Barros, Anne & Coit, David, 2023. "Dependent failure behavior modeling for risk and reliability: A systematic and critical literature review," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    20. Yousefi, Nooshin & Coit, David W. & Song, Sanling, 2020. "Reliability analysis of systems considering clusters of dependent degrading components," Reliability Engineering and System Safety, Elsevier, vol. 202(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:sae:risrel:v:233:y:2019:i:2:p:175-185. 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: SAGE Publications (email available below). General contact details of provider: .

    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.