IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v218y2022ipbs0951832021006396.html
   My bibliography  Save this article

Dynamic failure mode analysis approach based on an improved Taguchi process capability index

Author

Listed:
  • Li, Wanhong
  • Liu, Guangzhong

Abstract

Evaluating real-time operation state, diagnosing faults, and improving the operation reliability are important for the entire life cycle of equipment. This study proposes a dynamic failure mode analysis (DFMA) approach based on an improved Taguchi process capability index (PCI) and has three possible contributions. First, an improved Taguchi PCI, namely, Cpm-Q, and a new fault risk index (FRI), namely, Rpm-Q, are proposed to measure the dynamic fault risk of structural components. Second, dynamic risk priority number (DRPN) is proposed by integrating the fault importance index (FII) and FRI. Third, a real-time state representation based on FRI and DRPN, which divide the operation risk of the equipment into six state intervals, is proposed. We applied the approach on particle accelerator cooling water equipment located at Shanghai proton and heavy ion center. Results show that the approach is effective for real-time operation state assessment and fault identification.

Suggested Citation

  • Li, Wanhong & Liu, Guangzhong, 2022. "Dynamic failure mode analysis approach based on an improved Taguchi process capability index," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
  • Handle: RePEc:eee:reensy:v:218:y:2022:i:pb:s0951832021006396
    DOI: 10.1016/j.ress.2021.108152
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021006396
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2021.108152?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. Carazas, F.G. & Souza, G.F.M., 2010. "Risk-based decision making method for maintenance policy selection of thermal power plant equipment," Energy, Elsevier, vol. 35(2), pages 964-975.
    2. Cai, Wei & Zhao, Jingyi & Zhu, Ming, 2020. "A real time methodology of cluster-system theory-based reliability estimation using k-means clustering," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    3. Huang, Jia & You, Jian-Xin & Liu, Hu-Chen & Song, Ming-Shun, 2020. "Failure mode and effect analysis improvement: A systematic literature review and future research agenda," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    4. Ding Zhang & Yingjie Zhang & Mingrang Yu & Yun Chen, 2017. "Reliability evaluation and component importance measure for manufacturing systems based on failure losses," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1859-1869, December.
    5. Peng, Rui & Liu, Bin & Zhai, Qingqing & Wang, Wenbin, 2019. "Optimal maintenance strategy for systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 624-632.
    6. Du, Zhimin & Chen, Ling & Jin, Xinqiao, 2017. "Data-driven based reliability evaluation for measurements of sensors in a vapor compression system," Energy, Elsevier, vol. 122(C), pages 237-248.
    7. Lu, Zhenzhou & Xinyao Li,, 2018. "Failure-mode importance measures in structural system with multiple failure modes and its estimation using copulaAuthor-Name: He, Liangli," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 53-59.
    8. Duan, Chaoqun & Makis, Viliam & Deng, Chao, 2020. "A two-level Bayesian early fault detection for mechanical equipment subject to dependent failure modes," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    9. Tomohiro, Ryosuke & Arizono, Ikuo & Takemoto, Yasuhiko, 2020. "Economic design of double sampling Cpm control chart for monitoring process capability," International Journal of Production Economics, Elsevier, vol. 221(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. Bhattacharjee, Pushparenu & Dey, Vidyut & Mandal, U.K. & Paul, Susmita, 2022. "Quantitative risk assessment of submersible pump components using Interval number-based Multinomial Logistic Regression(MLR) model," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Zhou, Han & Yin, Hongpeng & Chai, Yi, 2023. "Multi-grained mode partition and robust fault diagnosis for multimode industrial processes," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Martínez-Galán Fernández, Pablo & Guillén López, Antonio J. & Márquez, Adolfo Crespo & Gomez Fernández, Juan Fco. & Marcos, Jose Antonio, 2022. "Dynamic Risk Assessment for CBM-based adaptation of maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

    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. Peng, Rui & He, Xiaofeng & Zhong, Chao & Kou, Gang & Xiao, Hui, 2022. "Preventive maintenance for heterogeneous parallel systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Zhang, Fengxia & Shen, Jingyuan & Liao, Haitao & Ma, Yizhong, 2021. "Optimal preventive maintenance policy for a system subject to two-phase imperfect inspections," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. Tang, Ming & Liao, Huchang, 2021. "Failure mode and effect analysis considering the fairness-oriented consensus of a large group with core-periphery structure," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Wang, Tianzhe & Chen, Zequan & Li, Guofa & He, Jialong & Liu, Chao & Du, Xuejiao, 2024. "A novel method for high-dimensional reliability analysis based on activity score and adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Yuan, Zixia & Xiong, Guojiang & Fu, Xiaofan & Mohamed, Ali Wagdy, 2023. "Improving fault tolerance in diagnosing power system failures with optimal hierarchical extreme learning machine," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    6. Lu, Shaoqi & Shi, Daimin & Xiao, Hui, 2019. "Reliability of sliding window systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 366-376.
    7. Wu, Jingyao & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Li, Haihe & Wang, Pan & Huang, Xiaoyu & Zhang, Zheng & Zhou, Changcong & Yue, Zhufeng, 2021. "Vine copula-based parametric sensitivity analysis of failure probability-based importance measure in the presence of multidimensional dependencies," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
    10. Kampitsis, Dimitris & Panagiotidou, Sofia, 2022. "A Bayesian condition-based maintenance and monitoring policy with variable sampling intervals," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    11. Ranka Gojković & Goran Đurić & Danijela Tadić & Snežana Nestić & Aleksandar Aleksić, 2021. "Evaluation and Selection of the Quality Methods for Manufacturing Process Reliability Improvement—Intuitionistic Fuzzy Sets and Genetic Algorithm Approach," Mathematics, MDPI, vol. 9(13), pages 1-17, June.
    12. Wu, Shengna & Yang, Jun & Peng, Rui & Zhai, Qingqing, 2021. "Optimal design of facility allocation and maintenance strategy for a cellular network," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    13. Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. Martyna Tomala & Andrzej Rusin & Adam Wojaczek, 2020. "Risk-Based Planning of Diagnostic Testing of Turbines Operating with Increased Flexibility," Energies, MDPI, vol. 13(13), pages 1-16, July.
    15. Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    16. Jung, Sejin & Yoo, Junbeom & Lee, Young-Jun, 2020. "A practical application of NUREG/CR-6430 software safety hazard analysis to FPGA software," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    17. Su, Yunsheng & Shi, Luojie & Zhou, Kai & Bai, Guangxing & Wang, Zequn, 2024. "Knowledge-informed deep networks for robust fault diagnosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    18. Cheng, Yao & Wei, Yian & Liao, Haitao, 2022. "Optimal sampling-based sequential inspection and maintenance plans for a heterogeneous product with competing failure modes," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    19. Scholtens, Bert & Boersen, Arieke, 2011. "Stocks and energy shocks: The impact of energy accidents on stock market value," Energy, Elsevier, vol. 36(3), pages 1698-1702.
    20. Yuyama, Ayumi & Kajitani, Yoshio & Shoji, Gaku, 2018. "Simulation of operational reliability of thermal power plants during a power crisis: Are we underestimating power shortage risk?," Applied Energy, Elsevier, vol. 231(C), pages 901-913.

    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:reensy:v:218:y:2022:i:pb:s0951832021006396. 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: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.