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RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT)

Ji xie qiang du, 2023-01, p.1-8 [Peer Reviewed Journal]

ISSN: 1001-9669

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  • Title:
    RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT)
  • Author: ZHOU FuMing ; LIU WuQiang ; YANG XiaoQiang ; SHEN JinXing ; CHEN ZhaoYi
  • Subjects: Refined improved multiscale fast sample entropy; Max-relevance and min-redundancy; Support vector machine classifier; Rotating machinery; Fault diagnosis
  • Is Part Of: Ji xie qiang du, 2023-01, p.1-8
  • Description: To solve the problems of low computational efficiency and missing amplitude information existing in the current multiscale sample entropy（MSE） method when extracting features of complex series, refined improved multiscale fast sample entropy（RIMFSE） is presented. Firstly, fast sample entropy is employed to substitute traditional sample entropy, and the calculation cost is greatly reduced by improving the matching mechanism of reconstructed vectors. After that, the improved multiscale expansion method is applied to replace the traditional coarse-grained method, thereby avoiding the loss of amplitude information. Based on this, a new rotating machinery fault diagnosis method is proposed in combination with the max-relevance and min-redundancy（mRMR） method and the support vector machine（SVM） classifier. Two fault data sets of gearbox and bearing are used to verify the performance of the presented method; meanwhile, the presented method is compared with existing methods such as MSE, composite MSE（CMSE） and refine
  • Publisher: Editorial Office of Journal of Mechanical Strength
  • Language: Chinese
  • Identifier: ISSN: 1001-9669
  • Source: DOAJ Directory of Open Access Journals
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