IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i2p398-d1033439.html
   My bibliography  Save this article

Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions

Author

Listed:
  • Hyewon Lee

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Izaz Raouf

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Jinwoo Song

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Heung Soo Kim

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Soobum Lee

    (Department of Mechanical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA)

Abstract

A robot is essential in many industrial and manufacturing facilities due to its efficiency, accuracy, and durability. However, continuous use of the robotic system can result in various component failures. The servo motor is one of the critical components, and its bearing is one of the vulnerable parts, hence failure analysis is required. Some previous prognostics and health management (PHM) methods are very limited in considering the realistic operating conditions of industrial robots based on various operating speeds, loading conditions, and motions, because they consider constant speed data with unloading conditions. This paper implements a PHM for the servo motor of a robotic arm based on variable operating conditions. Principal component analysis-based dimensionality reduction and correlation analysis-based feature selection are compared. Two machine learning algorithms have been used to detect fault features under various operating conditions. This method is proposed as a robust fault-detection model for industrial robots under various operating conditions. Features from different domains not only improved the generalization of the model’s performance but also improved the computational efficiency of massive data by reducing the total number of features. The results showed more than 90% accuracy under various operating conditions. As a result, the proposed method shows the possibility of robust failure diagnosis under various operating conditions similar to the actual industrial environment.

Suggested Citation

  • Hyewon Lee & Izaz Raouf & Jinwoo Song & Heung Soo Kim & Soobum Lee, 2023. "Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions," Mathematics, MDPI, vol. 11(2), pages 1-17, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:398-:d:1033439
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/2/398/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/2/398/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Jiang, Yuchen & Luo, Hao & Yin, Shen, 2023. "A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2020. "Time-variant reliability analysis for industrial robot RV reducer under multiple failure modes using Kriging model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    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. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.

    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. Yang, Meide & Zhang, Dequan & Jiang, Chao & Han, Xu & Li, Qing, 2021. "A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Jiang, Chen & Yan, Yifang & Wang, Dapeng & Qiu, Haobo & Gao, Liang, 2021. "Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    4. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Subramanian, Abhinav & Mahadevan, Sankaran, 2022. "Importance sampling for probabilistic prognosis of sector-wide flight separation safety," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Zhujun Wang & Qin Su & Bi Wang & Jie Wang, 2023. "Improving Lithium-Ion Battery Supply Chain Information Security by User Behavior Monitoring Algorithm Incorporated in Cloud Enterprise Resource Planning," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    7. Yaqun, Qi & Ping, Jin & Ruizhi, Li & Sheng, Zhang & Guobiao, Cai, 2020. "Dynamic reliability analysis for the reusable thrust chamber: A multi-failure modes investigation based on coupled thermal-structural analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    8. Deli Zhang & Zhaopeng Dong & Feifei Bu & Zijie Gu & Zitao Guo, 2023. "High-Precision Acquisition Method of Position Signal of Permanent Magnet Direct Drive Servo Motor at Low Speed," Energies, MDPI, vol. 16(11), pages 1-15, June.
    9. Yang, Bin & Yang, Wenyu, 2023. "Modular approach to kinematic reliability analysis of industrial robots," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    10. Alfarizi, Muhammad Gibran & Ustolin, Federico & Vatn, Jørn & Yin, Shen & Paltrinieri, Nicola, 2023. "Towards accident prevention on liquid hydrogen: A data-driven approach for releases prediction," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    11. Chen, Dingliang & Cai, Wei & Yu, Hangjun & Wu, Fei & Qin, Yi, 2023. "A novel transfer gear life prediction method by the cross-condition health indicator and nested hierarchical binary-valued network," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    12. Chen, Junhua & Chen, Longmiao & Qian, Linfang & Chen, Guangsong & Zhou, Shijie, 2022. "Time-dependent kinematic reliability analysis of gear mechanism based on sequential decoupling strategy and saddle-point approximation," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    13. Zhang, Zhiyao & Chen, Xiaohui & Zio, Enrico & Li, Longxiao, 2023. "Multi-task learning boosted predictions of the remaining useful life of aero-engines under scenarios of working-condition shift," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    14. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2021. "Time-variant system reliability analysis method for a small failure probability problem," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    15. Ding, Peng & Zhao, Xiaoli & Shao, Haidong & Jia, Minping, 2023. "Machinery cross domain degradation prognostics considering compound domain shifts," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    16. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(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:2:p:398-:d:1033439. 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.