Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals
Abstract In order to make accurate judgements of rolling bearing main fault types using the small sample size fault data set, a novel approach is put forward that combines particle swarm optimisation kernel fuzzy C‐means (PSO‐KFCM) and variational mode decomposition (VMD). Firstly, by calculating th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2021-06-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12026 |
| _version_ | 1797429287341522944 |
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| author | Yong Chang Guangqing Bao Sikai Cheng Ting He Qiaoling Yang |
| author_facet | Yong Chang Guangqing Bao Sikai Cheng Ting He Qiaoling Yang |
| author_sort | Yong Chang |
| collection | DOAJ |
| description | Abstract In order to make accurate judgements of rolling bearing main fault types using the small sample size fault data set, a novel approach is put forward that combines particle swarm optimisation kernel fuzzy C‐means (PSO‐KFCM) and variational mode decomposition (VMD). Firstly, by calculating the centre frequency and Pearson correlation coefficient of each mode function of VMD, the decomposition level K of VMD is determined, and the optimal decomposition result is obtained. The singular value decomposition method was used to extract a characteristic value corresponding to the main fault types of bearings from the optimal decomposition results, and faulty feature sample space was established. Then, the kernel function parameters and the initial clustering centre were used as optimisation variables. The PSO algorithm was used to solve the clustering model. The clustering centre of each fault type under the optimal classification result was obtained, and the fault diagnosis model was established. Finally, different fault classification methods are compared, and the conclusions drawn from the experiment show that the method can achieve good results in bearing fault diagnosis. The accuracy of fault classification was improved obviously. |
| first_indexed | 2024-03-09T09:11:00Z |
| format | Article |
| id | doaj.art-743deb19af694bfeade65be8bbcc46b5 |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T09:11:00Z |
| publishDate | 2021-06-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-743deb19af694bfeade65be8bbcc46b52023-12-02T08:33:51ZengHindawi-IETIET Signal Processing1751-96751751-96832021-06-0115423825010.1049/sil2.12026Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signalsYong Chang0Guangqing Bao1Sikai Cheng2Ting He3Qiaoling Yang4College of Electrical and Information Engineering, Lanzhou University of Technology Lanzhou ChinaCollege of Electrical and Information Engineering, Lanzhou University of Technology Lanzhou ChinaThe Engineering Department the University of Melbourne Melbourne AustraliaGansu Natural Energy Research Institute Lanzhou ChinaCollege of Electrical and Information Engineering, Lanzhou University of Technology Lanzhou ChinaAbstract In order to make accurate judgements of rolling bearing main fault types using the small sample size fault data set, a novel approach is put forward that combines particle swarm optimisation kernel fuzzy C‐means (PSO‐KFCM) and variational mode decomposition (VMD). Firstly, by calculating the centre frequency and Pearson correlation coefficient of each mode function of VMD, the decomposition level K of VMD is determined, and the optimal decomposition result is obtained. The singular value decomposition method was used to extract a characteristic value corresponding to the main fault types of bearings from the optimal decomposition results, and faulty feature sample space was established. Then, the kernel function parameters and the initial clustering centre were used as optimisation variables. The PSO algorithm was used to solve the clustering model. The clustering centre of each fault type under the optimal classification result was obtained, and the fault diagnosis model was established. Finally, different fault classification methods are compared, and the conclusions drawn from the experiment show that the method can achieve good results in bearing fault diagnosis. The accuracy of fault classification was improved obviously.https://doi.org/10.1049/sil2.12026fault diagnosisfuzzy set theoryparticle swarm optimisationpattern clusteringrolling bearingssignal classification |
| spellingShingle | Yong Chang Guangqing Bao Sikai Cheng Ting He Qiaoling Yang Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals IET Signal Processing fault diagnosis fuzzy set theory particle swarm optimisation pattern clustering rolling bearings signal classification |
| title | Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals |
| title_full | Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals |
| title_fullStr | Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals |
| title_full_unstemmed | Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals |
| title_short | Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals |
| title_sort | improved vmd kfcm algorithm for the fault diagnosis of rolling bearing vibration signals |
| topic | fault diagnosis fuzzy set theory particle swarm optimisation pattern clustering rolling bearings signal classification |
| url | https://doi.org/10.1049/sil2.12026 |
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