IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v311y2022i1d10.1007_s10479-019-03220-3.html
   My bibliography  Save this article

Recognition method of equipment state with the FLDA based Mahalanobis–Taguchi system

Author

Listed:
  • Ning Wang

    (Chang’an University)

  • Zhuo Zhang

    (Northwestern Polytechnical University)

  • Jiao Zhao

    (Chang’an University)

  • Dawei Hu

    (Chang’an University)

Abstract

Mahalanobis–Taguchi system (MTS) is a kind of big data classification and reduction method which can be used in the fault diagnosis and maintenance modeling. Especially in the context of big data, it can get better results in application. And MTS uses Mahalanobis distance (MD) as the measurement scale to identify the system state with multidimensional characteristics. But when the benchmark and abnormal space which are constructed by the traditional MTS have a serious overlap, the model will perform imbalanced classification ability to identify the sample. In this paper, against the problem, a modified MTS amended by Fischer linear discriminant analysis (FLDA) is proposed, and to be used to recognize the running state of equipment. Firstly, the paper discussed the limitation to using MD as the measurement scale in the traditional model, and then to use the balance accuracy while balanced classification as the evaluation index for the balance ability of the model classification. And then the threshold optimization model was discussed with different weight coefficient considering the actual cost and loss of the missed-alarm and the false-alarm. Furthermore, FLDA was used to calculate the projection matrix and the best projection vector was selected to amend the tradition measurement scale. Finally, the modified model amended by FLDA was compared with the traditional MTS and FLDA model form two aspects of accuracy index and the size of abnormal samples by using the bearing running data. The result proved the effectiveness and superiority of the modified model.

Suggested Citation

  • Ning Wang & Zhuo Zhang & Jiao Zhao & Dawei Hu, 2022. "Recognition method of equipment state with the FLDA based Mahalanobis–Taguchi system," Annals of Operations Research, Springer, vol. 311(1), pages 417-435, April.
    • Handle: RePEc:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-019-03220-3
    DOI: 10.1007/s10479-019-03220-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-019-03220-3
    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/s10479-019-03220-3?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. Ning Wang & Can Saygin & Shu-dong Sun, 2013. "Impact of Mahalanobis space construction on effectiveness of Mahalanobis-Taguchi system," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 13(2), pages 233-249.
    2. Ya-Ju Fan & Wanpracha Chaovalitwongse, 2010. "Optimizing feature selection to improve medical diagnosis," Annals of Operations Research, Springer, vol. 174(1), pages 169-183, February.
    3. Mi, Jinhua & Li, Yan-Feng & Yang, Yuan-Jian & Peng, Weiwen & Huang, Hong-Zhong, 2016. "Reliability assessment of complex electromechanical systems under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 1-15.
    4. Mi, Jinhua & Li, Yan-Feng & Peng, Weiwen & Huang, Hong-Zhong, 2018. "Reliability analysis of complex multi-state system with common cause failure based on evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 71-81.
    5. Marie-Laure Bougnol & José Dulá, 2006. "Validating DEA as a ranking tool: An application of DEA to assess performance in higher education," Annals of Operations Research, Springer, vol. 145(1), pages 339-365, July.
    6. Junxun Chen & Longsheng Cheng & Hui Yu & Shaolin Hu, 2018. "Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(1), pages 147-159, January.
    7. Chi-Feng Peng & Li-Hsing Ho & Sang-Bing Tsai & Yin-Cheng Hsiao & Yuming Zhai & Quan Chen & Li-Chung Chang & Zhiwen Shang, 2017. "Applying the Mahalanobis–Taguchi System to Improve Tablet PC Production Processes," Sustainability, MDPI, vol. 9(9), pages 1-17, September.
    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. Sangho Lee & Jeongsub Choi & Youngdoo Son, 2024. "Efficient visibility algorithm for high-frequency time-series: application to fault diagnosis with graph convolutional network," Annals of Operations Research, Springer, vol. 339(1), pages 813-833, August.

    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. Zheng Liu & Xin Liu & Hong-Zhong Huang & Pingyu Zhu & Zhongwei Liang, 2022. "A new inherent reliability modeling and analysis method based on imprecise Dirichlet model for machine tool spindle," Annals of Operations Research, Springer, vol. 311(1), pages 295-310, April.
    2. Yuan-Jian Yang & Ya-Lan Xiong & Xin-Yin Zhang & Gui-Hua Wang & Bihai Zou, 2022. "Reliability analysis of continuous emission monitoring system with common cause failure based on fuzzy FMECA and Bayesian networks," Annals of Operations Research, Springer, vol. 311(1), pages 451-467, April.
    3. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Wang, Ning, 2024. "Algorithms for Bayesian network modeling and reliability inference of complex multistate systems with common cause failure," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Mi, Jinhua & Beer, Michael & Li, Yan-Feng & Broggi, Matteo & Cheng, Yuhua, 2020. "Reliability and importance analysis of uncertain system with common cause failures based on survival signature," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Jinhua Mi & Yuhua Cheng & Yufei Song & Libing Bai & Kai Chen, 2022. "Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions," Annals of Operations Research, Springer, vol. 311(1), pages 311-333, April.
    6. Yingchun Xu & Xiaohu Zheng & Wen Yao & Ning Wang & Xiaoqian Chen, 2021. "A sequential multi-prior integration and updating method for complex multi-level system based on Bayesian melding method," Journal of Risk and Reliability, , vol. 235(5), pages 863-876, October.
    7. Zeng, Ying & Huang, Tudi & Li, Yan-Feng & Huang, Hong-Zhong, 2023. "Reliability modeling for power converter in satellite considering periodic phased mission," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    8. Song, Yufei & Mi, Jinhua & Cheng, Yuhua & Bai, Libing & Chen, Kai, 2020. "A dependency bounds analysis method for reliability assessment of complex system with hybrid uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. Yong-Hua Li & Fu-Yu Zhao & Yue-Hua Gao & Peng-Peng Zhi, 2022. "Importance analysis of underframe connection system for the pantograph lower arm rod," Annals of Operations Research, Springer, vol. 311(1), pages 211-223, April.
    10. 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).
    11. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Chen, Xianqi, 2019. "Improved compression inference algorithm for reliability analysis of complex multistate satellite system based on multilevel Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 123-142.
    12. Ying-Kui Gu & Chao-Jun Fan & Ling-Qiang Liang & Jun Zhang, 2022. "Reliability calculation method based on the Copula function for mechanical systems with dependent failure," Annals of Operations Research, Springer, vol. 311(1), pages 99-116, April.
    13. Yan-Feng Li & Hong-Zhong Huang & Jinhua Mi & Weiwen Peng & Xiaomeng Han, 2022. "Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability," Annals of Operations Research, Springer, vol. 311(1), pages 195-209, April.
    14. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    15. Guang-Jun Jiang & Hong-Xia Chen & Le Gao & Hong-Hua Sun & Qing-Yang Li, 2022. "Reliability analysis on ammonium nitrate/fuel oil explosive vehicle pharmaceutical system based on dynamic fault tree and Bayesian network," Annals of Operations Research, Springer, vol. 311(1), pages 167-182, April.
    16. Li, Xiang-Yu & Xiong, Xiaoyan & Guo, Junyu & Huang, Hong-Zhong & Li, Xiaopeng, 2022. "Reliability assessment of non-repairable multi-state phased mission systems with backup missions," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    17. Li, Xiang-Yu & Li, Yan-Feng & Huang, Hong-Zhong & Zio, Enrico, 2018. "Reliability assessment of phased-mission systems under random shocks," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 352-361.
    18. Jie Zhou & Hong-Zhong Huang & Yan-Feng Li & Junyu Guo, 2022. "A framework for fatigue reliability analysis of high-pressure turbine blades," Annals of Operations Research, Springer, vol. 311(1), pages 489-505, April.
    19. Zhang, Xiaoqiang & Gao, Huiying & Huang, Hong-Zhong & Li, Yan-Feng & Mi, Jinhua, 2018. "Dynamic reliability modeling for system analysis under complex load," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 345-351.
    20. Jianxiong Gao & Zongwen An & Xuezong Bai, 2022. "A new representation method for probability distributions of multimodal and irregular data based on uniform mixture model," Annals of Operations Research, Springer, vol. 311(1), pages 81-97, April.

    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:annopr:v:311:y:2022:i:1:d:10.1007_s10479-019-03220-3. 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: 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.