RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT)
In order to extract the fault information of rolling bearing vibration signals under strong noise coverage and improve the accuracy of fault diagnosis and classification, based on the theory of fuzzy entropy（FE）, a new method of complete ensemble empirical mode decomposition with adaptive noise（CEEM...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2023-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.01.004 |
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| _version_ | 1841534326199549952 |
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| author | XIAO JunQing JIN JiangTao LI Chun XU ZiFei SUN Kang |
| author_facet | XIAO JunQing JIN JiangTao LI Chun XU ZiFei SUN Kang |
| author_sort | XIAO JunQing |
| collection | DOAJ |
| description | In order to extract the fault information of rolling bearing vibration signals under strong noise coverage and improve the accuracy of fault diagnosis and classification, based on the theory of fuzzy entropy（FE）, a new method of complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN） and convolutional neural network（CNN） is proposed, which make full use of the independence, relative consistency, and the advantages of fuzzy entropy and randomness. The fuzzy entropy of the original signal was obtained by cyclic sampling, decomposed by CEEMDAN method, and the optimal component group was screened by Pearson correlation coefficient. Finally, the optimal component group was input to CNN for fault diagnosis, and the t-SNE popular learning algorithm was used for clustering visualization. The results show that compared with EMD-Fuzzy Entropy and EEMD-Fuzzy Entropy under different working conditions, the proposed method has stronger robustness and generalization, and t-SNE visualization makes the results more intuitive. |
| format | Article |
| id | doaj-art-68e1e52147654b678dab7a8ac611db5f |
| 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-68e1e52147654b678dab7a8ac611db5f2025-01-15T02:40:26ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692023-01-01263336350274RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT)XIAO JunQingJIN JiangTaoLI ChunXU ZiFeiSUN KangIn order to extract the fault information of rolling bearing vibration signals under strong noise coverage and improve the accuracy of fault diagnosis and classification, based on the theory of fuzzy entropy（FE）, a new method of complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN） and convolutional neural network（CNN） is proposed, which make full use of the independence, relative consistency, and the advantages of fuzzy entropy and randomness. The fuzzy entropy of the original signal was obtained by cyclic sampling, decomposed by CEEMDAN method, and the optimal component group was screened by Pearson correlation coefficient. Finally, the optimal component group was input to CNN for fault diagnosis, and the t-SNE popular learning algorithm was used for clustering visualization. The results show that compared with EMD-Fuzzy Entropy and EEMD-Fuzzy Entropy under different working conditions, the proposed method has stronger robustness and generalization, and t-SNE visualization makes the results more intuitive.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.01.004Convolutional neural networkBearingCEEMDANFuzzy entropyFault diagnosis |
| spellingShingle | XIAO JunQing JIN JiangTao LI Chun XU ZiFei SUN Kang RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT) Jixie qiangdu Convolutional neural network Bearing CEEMDAN Fuzzy entropy Fault diagnosis |
| title | RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT) |
| title_full | RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT) |
| title_fullStr | RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT) |
| title_full_unstemmed | RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT) |
| title_short | RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT) |
| title_sort | research on bearing fault diagnosis based on ceemdan fuzzy entropy and convolutional neural network mt |
| topic | Convolutional neural network Bearing CEEMDAN Fuzzy entropy Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.01.004 |
| work_keys_str_mv | AT xiaojunqing researchonbearingfaultdiagnosisbasedonceemdanfuzzyentropyandconvolutionalneuralnetworkmt AT jinjiangtao researchonbearingfaultdiagnosisbasedonceemdanfuzzyentropyandconvolutionalneuralnetworkmt AT lichun researchonbearingfaultdiagnosisbasedonceemdanfuzzyentropyandconvolutionalneuralnetworkmt AT xuzifei researchonbearingfaultdiagnosisbasedonceemdanfuzzyentropyandconvolutionalneuralnetworkmt AT sunkang researchonbearingfaultdiagnosisbasedonceemdanfuzzyentropyandconvolutionalneuralnetworkmt |