Evaluation method of spindle performance degradation based on VMD and random forests
As one of the core components of the machine tool, the reliability of spindle is very important for improving machine tool quality. To effectively evaluate the performance degradation degree of the spindle, a method of degradation evaluation of the spindle performance based on variational mode decom...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-11-01
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| Series: | The Journal of Engineering |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9127 |
| _version_ | 1811270794383196160 |
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| author | Pangshi Wei Hongjun Wang |
| author_facet | Pangshi Wei Hongjun Wang |
| author_sort | Pangshi Wei |
| collection | DOAJ |
| description | As one of the core components of the machine tool, the reliability of spindle is very important for improving machine tool quality. To effectively evaluate the performance degradation degree of the spindle, a method of degradation evaluation of the spindle performance based on variational mode decomposition (VMD) and random forests (RFs) was proposed. Firstly, VMD is used to process the current signal to obtain several modal components. Then, the time domain and frequency domain features of each modes component are calculated as eigenvalues. Finally, the RFs algorithm is used to classify the eigenvalues. The experimental results show that VMD can decompose the signal better and avoid the phenomenon of the modal mixture. The combination of VMD and RFs can accurately and effectively evaluate the performance degradation of the spindle. |
| first_indexed | 2024-04-12T22:09:46Z |
| format | Article |
| id | doaj.art-435ed5ba6dcd4fdc80f7f690d5d6e4d5 |
| institution | Directory Open Access Journal |
| issn | 2051-3305 |
| language | English |
| last_indexed | 2024-04-12T22:09:46Z |
| publishDate | 2019-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Journal of Engineering |
| spelling | doaj.art-435ed5ba6dcd4fdc80f7f690d5d6e4d52022-12-22T03:14:49ZengWileyThe Journal of Engineering2051-33052019-11-0110.1049/joe.2018.9127JOE.2018.9127Evaluation method of spindle performance degradation based on VMD and random forestsPangshi Wei0Hongjun Wang1School of Mechanical and Electrical Engineering, Beijing Information Science and Technology UniversitySchool of Mechanical and Electrical Engineering, Beijing Information Science and Technology UniversityAs one of the core components of the machine tool, the reliability of spindle is very important for improving machine tool quality. To effectively evaluate the performance degradation degree of the spindle, a method of degradation evaluation of the spindle performance based on variational mode decomposition (VMD) and random forests (RFs) was proposed. Firstly, VMD is used to process the current signal to obtain several modal components. Then, the time domain and frequency domain features of each modes component are calculated as eigenvalues. Finally, the RFs algorithm is used to classify the eigenvalues. The experimental results show that VMD can decompose the signal better and avoid the phenomenon of the modal mixture. The combination of VMD and RFs can accurately and effectively evaluate the performance degradation of the spindle.https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9127vibrationsfrequency-domain analysismachine tool spindlestime-domain analysiseigenvalues and eigenfunctionsmachine toolsfeature extractionmechanical engineering computingcondition monitoringrf algorithmeigenvaluesmodes componentfrequency domain featurestime domainmodal componentsvariational mode decompositiondegradation evaluationperformance degradation degreemachine tool qualitycore componentsrandom forestsspindle performance degradationvmd |
| spellingShingle | Pangshi Wei Hongjun Wang Evaluation method of spindle performance degradation based on VMD and random forests The Journal of Engineering vibrations frequency-domain analysis machine tool spindles time-domain analysis eigenvalues and eigenfunctions machine tools feature extraction mechanical engineering computing condition monitoring rf algorithm eigenvalues modes component frequency domain features time domain modal components variational mode decomposition degradation evaluation performance degradation degree machine tool quality core components random forests spindle performance degradation vmd |
| title | Evaluation method of spindle performance degradation based on VMD and random forests |
| title_full | Evaluation method of spindle performance degradation based on VMD and random forests |
| title_fullStr | Evaluation method of spindle performance degradation based on VMD and random forests |
| title_full_unstemmed | Evaluation method of spindle performance degradation based on VMD and random forests |
| title_short | Evaluation method of spindle performance degradation based on VMD and random forests |
| title_sort | evaluation method of spindle performance degradation based on vmd and random forests |
| topic | vibrations frequency-domain analysis machine tool spindles time-domain analysis eigenvalues and eigenfunctions machine tools feature extraction mechanical engineering computing condition monitoring rf algorithm eigenvalues modes component frequency domain features time domain modal components variational mode decomposition degradation evaluation performance degradation degree machine tool quality core components random forests spindle performance degradation vmd |
| url | https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9127 |
| work_keys_str_mv | AT pangshiwei evaluationmethodofspindleperformancedegradationbasedonvmdandrandomforests AT hongjunwang evaluationmethodofspindleperformancedegradationbasedonvmdandrandomforests |