IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i8p1837-d1122077.html
   My bibliography  Save this article

Prediction of Tool Remaining Useful Life Based on NHPP-WPHM

Author

Listed:
  • Yingzhi Zhang

    (Key Laboratory of Reliability of CNC Equipment, Ministry of Education, No. 5988 Renmin Street, Nanguan, Changchun 130022, China
    School of Mechanical and Aerospace Engineering, Jilin University, No. 5988 Renmin Street, Nanguan, Changchun 130022, China)

  • Guiming Guo

    (Key Laboratory of Reliability of CNC Equipment, Ministry of Education, No. 5988 Renmin Street, Nanguan, Changchun 130022, China
    School of Mechanical and Aerospace Engineering, Jilin University, No. 5988 Renmin Street, Nanguan, Changchun 130022, China)

  • Fang Yang

    (China FAW Group Co., Ltd., Changchun 130011, China)

  • Yubin Zheng

    (Key Laboratory of Reliability of CNC Equipment, Ministry of Education, No. 5988 Renmin Street, Nanguan, Changchun 130022, China
    School of Mechanical and Aerospace Engineering, Jilin University, No. 5988 Renmin Street, Nanguan, Changchun 130022, China)

  • Fenli Zhai

    (Huawei Technologies Co., Ltd., Changchun 130012, China)

Abstract

A tool remaining useful life prediction method based on a non-homogeneous Poisson process and Weibull proportional hazard model (WPHM) is proposed, taking into account the grinding repair of machine tools during operation. The intrinsic failure rate model is built according to the tool failure data. The WPHM is established by collecting vibration information during operation and introducing covariates to describe the failure rate of the tool operation. In combination with the tool grinding repair, the NHPP-WPHM under different repair times is established to describe the tool comprehensive failure rate. The failure threshold of the tool life is determined by the maximum availability, and the remaining tool life is predicted. Take the cylindrical turning tool of the CNC lathe as an example, the root mean square error, mean absolute error, mean absolute percentage error, and determination coefficient (R 2 ) are used as indicators. The proposed method is compared with the actual remaining useful life and the remaining useful life prediction model based on the WPHM to verify the effectiveness of the model.

Suggested Citation

  • Yingzhi Zhang & Guiming Guo & Fang Yang & Yubin Zheng & Fenli Zhai, 2023. "Prediction of Tool Remaining Useful Life Based on NHPP-WPHM," Mathematics, MDPI, vol. 11(8), pages 1-17, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1837-:d:1122077
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/8/1837/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/8/1837/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Junxing & Wang, Zhihua & Zhang, Yongbo & Liu, Chengrui & Fu, Huimin, 2018. "A nonlinear Wiener process degradation model with autoregressive errors," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 48-57.
    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. Zhengyang Fan & Wanru Li & Kuo-Chu Chang, 2023. "A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation," Mathematics, MDPI, vol. 11(24), pages 1-17, December.
    2. Qi Lu & Xubo Gao & Felix T. S. Chan, 2023. "Low-Carbon Optimization Design of Grinding Machine Spindle Based on Improved Whale Algorithm," Mathematics, MDPI, vol. 12(1), pages 1-19, December.

    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. Wang, Xiaofei & Wang, Bing Xing & Jiang, Pei Hua & Hong, Yili, 2020. "Accurate reliability inference based on Wiener process with random effects for degradation data," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. Hao, Songhua & Yang, Jun & Berenguer, Christophe, 2019. "Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 261-270.
    3. Rocchetta, Roberto & Zhan, Zhouzhao & van Driel, Willem Dirk & Di Bucchianico, Alessandro, 2024. "Uncertainty analysis and interval prediction of LEDs lifetimes," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Zhang, Fode & Ng, Hon Keung Tony & Shi, Yimin, 2020. "Mis-specification analysis of Wiener degradation models by using f-divergence with outliers," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Duan, Fengjun & Wang, Guanjun, 2022. "Bayesian analysis for the transformed exponential dispersion process with random effects," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

    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:gam:jmathe:v:11:y:2023:i:8:p:1837-:d:1122077. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.