Bearing Fault Diagnosis Method based on RLMD and Kmeans++
To improve the performance of bearing fault diagnosis， a bearing fault diagnosis method based on Robust Local Mean Decomposition （RLMD） and Kmeans++ is proposed. The product functions （PF） are obtained by decomposing the bearing vibration signal using the RLMD technique. The sensitive PF components...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2021-02-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.02.025 |
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| _version_ | 1841547215458271232 |
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| author | Shaoting Yan Yuguo Zhou Yanbo Ren Shiliang Liu Shidang Yan |
| author_facet | Shaoting Yan Yuguo Zhou Yanbo Ren Shiliang Liu Shidang Yan |
| author_sort | Shaoting Yan |
| collection | DOAJ |
| description | To improve the performance of bearing fault diagnosis， a bearing fault diagnosis method based on Robust Local Mean Decomposition （RLMD） and Kmeans++ is proposed. The product functions （PF） are obtained by decomposing the bearing vibration signal using the RLMD technique. The sensitive PF components are sifted by calculating the correlation coefficients between the PF components and the original vibration signal， and the sensitive PF components are superimposed to form the reconstructed signal. The bearing fault feature set is formed by calculating the time and frequency domain statistical features of the original vibration signal and the reconstructed signal. The Fisher features of bearing failure feature are extracted by linear discriminant analysis （LDA）. The fault feature is clustered by the Kmeans++ clustering method and the cluster center of each bearing working condition is got. The bearing fault identification is realized by calculating the Hamming approach degree between the test sample and the cluster center. The simulated bearing data with different signal-to-noise ratios and bearing data from the Paderborn university test bench are used to evaluate the effectiveness of the proposed method. Results show that the proposed method can accurately identify bearing faults with different categories and levels even though the number of training sample is small. |
| format | Article |
| id | doaj-art-b476061afb614d4a99f85b3842a72626 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2021-02-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-b476061afb614d4a99f85b3842a726262025-01-10T14:54:18ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392021-02-014516317029800312Bearing Fault Diagnosis Method based on RLMD and Kmeans++Shaoting YanYuguo ZhouYanbo RenShiliang LiuShidang YanTo improve the performance of bearing fault diagnosis， a bearing fault diagnosis method based on Robust Local Mean Decomposition （RLMD） and Kmeans++ is proposed. The product functions （PF） are obtained by decomposing the bearing vibration signal using the RLMD technique. The sensitive PF components are sifted by calculating the correlation coefficients between the PF components and the original vibration signal， and the sensitive PF components are superimposed to form the reconstructed signal. The bearing fault feature set is formed by calculating the time and frequency domain statistical features of the original vibration signal and the reconstructed signal. The Fisher features of bearing failure feature are extracted by linear discriminant analysis （LDA）. The fault feature is clustered by the Kmeans++ clustering method and the cluster center of each bearing working condition is got. The bearing fault identification is realized by calculating the Hamming approach degree between the test sample and the cluster center. The simulated bearing data with different signal-to-noise ratios and bearing data from the Paderborn university test bench are used to evaluate the effectiveness of the proposed method. Results show that the proposed method can accurately identify bearing faults with different categories and levels even though the number of training sample is small.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.02.025BearingFault diagnosisRobust local mean decomposition（RLMD）Linear discriminant analysis（LDA）Kmeans++Hamming approach degree |
| spellingShingle | Shaoting Yan Yuguo Zhou Yanbo Ren Shiliang Liu Shidang Yan Bearing Fault Diagnosis Method based on RLMD and Kmeans++ Jixie chuandong Bearing Fault diagnosis Robust local mean decomposition（RLMD） Linear discriminant analysis（LDA） Kmeans++ Hamming approach degree |
| title | Bearing Fault Diagnosis Method based on RLMD and Kmeans++ |
| title_full | Bearing Fault Diagnosis Method based on RLMD and Kmeans++ |
| title_fullStr | Bearing Fault Diagnosis Method based on RLMD and Kmeans++ |
| title_full_unstemmed | Bearing Fault Diagnosis Method based on RLMD and Kmeans++ |
| title_short | Bearing Fault Diagnosis Method based on RLMD and Kmeans++ |
| title_sort | bearing fault diagnosis method based on rlmd and kmeans |
| topic | Bearing Fault diagnosis Robust local mean decomposition（RLMD） Linear discriminant analysis（LDA） Kmeans++ Hamming approach degree |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.02.025 |
| work_keys_str_mv | AT shaotingyan bearingfaultdiagnosismethodbasedonrlmdandkmeans AT yuguozhou bearingfaultdiagnosismethodbasedonrlmdandkmeans AT yanboren bearingfaultdiagnosismethodbasedonrlmdandkmeans AT shiliangliu bearingfaultdiagnosismethodbasedonrlmdandkmeans AT shidangyan bearingfaultdiagnosismethodbasedonrlmdandkmeans |