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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
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    References listed on IDEAS

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    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.
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    5. 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.
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