Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction
Nowadays, the existing blind source separation (BSS) algorithms in rotating machinery fault diagnosis can hardly meet the demand of fast response, high stability, and low complexity simultaneously. Therefore, this paper proposes a spectrum correction based BSS algorithm. Through the incorporation of...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/9564938 |
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| _version_ | 1832564746419699712 |
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| author | Xiangdong Huang Xukang Jin Haipeng Fu |
| author_facet | Xiangdong Huang Xukang Jin Haipeng Fu |
| author_sort | Xiangdong Huang |
| collection | DOAJ |
| description | Nowadays, the existing blind source separation (BSS) algorithms in rotating machinery fault diagnosis can hardly meet the demand of fast response, high stability, and low complexity simultaneously. Therefore, this paper proposes a spectrum correction based BSS algorithm. Through the incorporation of FFT, spectrum correction, a screen procedure (consisting of frequency merging, candidate pattern selection, and single-source-component recognition), modified k-means based source number estimation, and mixing matrix estimation, the proposed BSS algorithm can accurately achieve harmonics sensing on field rotating machinery faults in case of short-sampled observations. Both numerical simulation and practical experiment verify the proposed BSS algorithm’s superiority in the recovery quality, stability to insufficient samples, and efficiency over the existing ICA-based methods. Besides rotating machinery fault diagnosis, the proposed BSS algorithm also possesses a vast potential in other harmonics-related application fields. |
| format | Article |
| id | doaj-art-4abae3f03a434d92b2f6fdd46ec268da |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-4abae3f03a434d92b2f6fdd46ec268da2025-02-03T01:10:14ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/95649389564938Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum CorrectionXiangdong Huang0Xukang Jin1Haipeng Fu2School of Electronic Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electronic Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electronic Information Engineering, Tianjin University, Tianjin 300072, ChinaNowadays, the existing blind source separation (BSS) algorithms in rotating machinery fault diagnosis can hardly meet the demand of fast response, high stability, and low complexity simultaneously. Therefore, this paper proposes a spectrum correction based BSS algorithm. Through the incorporation of FFT, spectrum correction, a screen procedure (consisting of frequency merging, candidate pattern selection, and single-source-component recognition), modified k-means based source number estimation, and mixing matrix estimation, the proposed BSS algorithm can accurately achieve harmonics sensing on field rotating machinery faults in case of short-sampled observations. Both numerical simulation and practical experiment verify the proposed BSS algorithm’s superiority in the recovery quality, stability to insufficient samples, and efficiency over the existing ICA-based methods. Besides rotating machinery fault diagnosis, the proposed BSS algorithm also possesses a vast potential in other harmonics-related application fields.http://dx.doi.org/10.1155/2016/9564938 |
| spellingShingle | Xiangdong Huang Xukang Jin Haipeng Fu Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction Shock and Vibration |
| title | Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction |
| title_full | Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction |
| title_fullStr | Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction |
| title_full_unstemmed | Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction |
| title_short | Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction |
| title_sort | short sampled blind source separation of rotating machinery signals based on spectrum correction |
| url | http://dx.doi.org/10.1155/2016/9564938 |
| work_keys_str_mv | AT xiangdonghuang shortsampledblindsourceseparationofrotatingmachinerysignalsbasedonspectrumcorrection AT xukangjin shortsampledblindsourceseparationofrotatingmachinerysignalsbasedonspectrumcorrection AT haipengfu shortsampledblindsourceseparationofrotatingmachinerysignalsbasedonspectrumcorrection |