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Health condition evaluation method for motorized spindle on the basis of optimised VMD and GMM-HMM

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Abstract

When the motorized spindles fail, the vibration signal at the bearings will contain the information related to the fault degree and operation condition. Extracting and utilising this information in an industrial environment with strong noise are the key issues of the health condition evaluation of the motorized spindles. In this paper, a health condition evaluation method for motorized spindles on the basis of optimised variational mode decomposition (VMD) and Gaussian mixture model-hidden markov model (GMM-HMM) is proposed. Firstly, using the composite index KEI as the fitness function, the parameters in the VMD are optimised by the sooty tern optimisation algorithm (STOA), and a low-dimensional feature matrix that can represent the health condition of a motorized spindle is further constructed. Secondly, the GMM-HMM of each health condition is trained, and the health condition of the motorized spindle is evaluated based on the library. Finally, the hybrid simulation signal is analysed to verify the effectiveness and superiority of the optimised VMD. The rotor unbalanced fault experiment is carried out by using the motorized spindle performance monitoring test platform. The proposed method is used to evaluate the health of the tested motorized spindle, and the results verify its superiority.

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Acknowledgements

This work was supported by Jilin Science and Technology Development Plan-Key R&D Program (20210201055GX).

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Author notes

  1. Guofa Li

    Present address: Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun, 130025, China

Authors and Affiliations

  1. Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun, 130025, China

    Haiji Yang, Jialong He & Liding Wang

  2. School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130025, China

    Haiji Yang, Guofa Li, Jialong He & Liding Wang

  3. Faw Jiefang Automot CO., LTD, Changchun, 130025, China

    Xinyu Nie

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  1. Haiji Yang
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Contributions

Haiji Yang: Background research, Data curation, Software, Validation writing-original draft, Editing. Guofa Li: Methodology, Review & editing, Supervision. Jialong He: Project administration, Funding acquisition. Liding Wang: Supervision. Xinyu Nie: Assist in experiment. All authors read and approved the final manuscript.

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Correspondence to Guofa Li or Jialong He.

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Yang, H., Li, G., He, J. et al. Health condition evaluation method for motorized spindle on the basis of optimised VMD and GMM-HMM. Int J Adv Manuf Technol 124, 4465–4477 (2023). https://doi.org/10.1007/s00170-022-10202-6

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