RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT)
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 empl...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2023-01-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.01.001 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841534317616955392 |
|---|---|
| author | ZHOU FuMing LIU WuQiang YANG XiaoQiang SHEN JinXing CHEN ZhaoYi |
| author_facet | ZHOU FuMing LIU WuQiang YANG XiaoQiang SHEN JinXing CHEN ZhaoYi |
| author_sort | ZHOU FuMing |
| collection | DOAJ |
| 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 refined composite MSE（RCMSE）. The results show that compared with MSE, CMSE and RCMSE, the proposed method enjoys significant advantages in terms of robustness, calculation efficiency and recognition accuracy, thereby providing a new idea for rotating machinery fault diagnosis based on entropy feature. |
| format | Article |
| id | doaj-art-48699ac2dbd1420c86337a5224d9c126 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2023-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-48699ac2dbd1420c86337a5224d9c1262025-01-15T02:40:22ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692023-01-011836345072RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT)ZHOU FuMingLIU WuQiangYANG XiaoQiangSHEN JinXingCHEN ZhaoYiTo 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 refined composite MSE（RCMSE）. The results show that compared with MSE, CMSE and RCMSE, the proposed method enjoys significant advantages in terms of robustness, calculation efficiency and recognition accuracy, thereby providing a new idea for rotating machinery fault diagnosis based on entropy feature.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.01.001Refined improved multiscale fast sample entropyMax-relevance and min-redundancySupport vector machine classifierRotating machineryFault diagnosis |
| spellingShingle | ZHOU FuMing LIU WuQiang YANG XiaoQiang SHEN JinXing CHEN ZhaoYi RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT) Jixie qiangdu Refined improved multiscale fast sample entropy Max-relevance and min-redundancy Support vector machine classifier Rotating machinery Fault diagnosis |
| title | RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT) |
| title_full | RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT) |
| title_fullStr | RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT) |
| title_full_unstemmed | RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT) |
| title_short | RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT) |
| title_sort | research on fault diagnosis method of rotating machinery based on refined improved multiscale fast sample entropy mt |
| topic | Refined improved multiscale fast sample entropy Max-relevance and min-redundancy Support vector machine classifier Rotating machinery Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.01.001 |
| work_keys_str_mv | AT zhoufuming researchonfaultdiagnosismethodofrotatingmachinerybasedonrefinedimprovedmultiscalefastsampleentropymt AT liuwuqiang researchonfaultdiagnosismethodofrotatingmachinerybasedonrefinedimprovedmultiscalefastsampleentropymt AT yangxiaoqiang researchonfaultdiagnosismethodofrotatingmachinerybasedonrefinedimprovedmultiscalefastsampleentropymt AT shenjinxing researchonfaultdiagnosismethodofrotatingmachinerybasedonrefinedimprovedmultiscalefastsampleentropymt AT chenzhaoyi researchonfaultdiagnosismethodofrotatingmachinerybasedonrefinedimprovedmultiscalefastsampleentropymt |