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

  • Ning WangEmail author
  • Zhuo Zhang
  • Jiao Zhao
  • Dawei Hu
S.I.: Reliability Modeling with Applications Based on Big Data
  • 9 Downloads

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.

Keywords

Big data Mahalanobis–Taguchi system Mahalanobis distance Balanced classification Fischer linear discriminant analysis Balance accuracy 

Notes

Acknowledgements

The authors would like to acknowledge the support of the National Natural Science Foundation of China (Grants No. 71401016), the Fundamental Research Funds for Central Universities of Chang’an University (Grants Nos. 300102228110, 300102228402).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Logistics Engineering, School of AutomobileChang’an UniversityXi’anPeople’s Republic of China
  2. 2.Department of Industrial Engineering, School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China

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