Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals
In the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation f...
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| Format: | Article |
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MDPI AG
2016-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/6/897 |
| _version_ | 1811298270573494272 |
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| author | Gang Tang Ganggang Luo Weihua Zhang Caijin Yang Huaqing Wang |
| author_facet | Gang Tang Ganggang Luo Weihua Zhang Caijin Yang Huaqing Wang |
| author_sort | Gang Tang |
| collection | DOAJ |
| description | In the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation for the vibration sources estimation, which makes it difficult to extract fault features exactly by ordinary methods in running tests. To improve the effectiveness of compound fault diagnosis in roller bearings, the present paper proposes a new method to solve the underdetermined problem and to extract fault features based on variational mode decomposition. In order to surmount the shortcomings of inadequate signals collected through limited sensors, a vibration signal is firstly decomposed into a number of band-limited intrinsic mode functions by variational mode decomposition. Then, the demodulated signal with the Hilbert transform of these multi-channel functions is used as the input matrix for independent component analysis. Finally, the compound faults are separated effectively by carrying out independent component analysis, which enables the fault features to be extracted more easily and identified more clearly. Experimental results validate the effectiveness of the proposed method in compound fault separation, and a comparison experiment shows that the proposed method has higher adaptability and practicability in separating strong noise signals than the commonly-used ensemble empirical mode decomposition method. |
| first_indexed | 2024-04-13T06:16:59Z |
| format | Article |
| id | doaj.art-1d60d0e590b04914876191da16cb77c5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:16:59Z |
| publishDate | 2016-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-1d60d0e590b04914876191da16cb77c52022-12-22T02:58:47ZengMDPI AGSensors1424-82202016-06-0116689710.3390/s16060897s16060897Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault SignalsGang Tang0Ganggang Luo1Weihua Zhang2Caijin Yang3Huaqing Wang4College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, ChinaCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, ChinaTraction Power State Key Laboratory, Southwest Jiaotong University, Chengdu 610031, ChinaTraction Power State Key Laboratory, Southwest Jiaotong University, Chengdu 610031, ChinaCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, ChinaIn the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation for the vibration sources estimation, which makes it difficult to extract fault features exactly by ordinary methods in running tests. To improve the effectiveness of compound fault diagnosis in roller bearings, the present paper proposes a new method to solve the underdetermined problem and to extract fault features based on variational mode decomposition. In order to surmount the shortcomings of inadequate signals collected through limited sensors, a vibration signal is firstly decomposed into a number of band-limited intrinsic mode functions by variational mode decomposition. Then, the demodulated signal with the Hilbert transform of these multi-channel functions is used as the input matrix for independent component analysis. Finally, the compound faults are separated effectively by carrying out independent component analysis, which enables the fault features to be extracted more easily and identified more clearly. Experimental results validate the effectiveness of the proposed method in compound fault separation, and a comparison experiment shows that the proposed method has higher adaptability and practicability in separating strong noise signals than the commonly-used ensemble empirical mode decomposition method.http://www.mdpi.com/1424-8220/16/6/897roller bearingfault diagnosisvariational mode decompositionindependent component analysis |
| spellingShingle | Gang Tang Ganggang Luo Weihua Zhang Caijin Yang Huaqing Wang Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals Sensors roller bearing fault diagnosis variational mode decomposition independent component analysis |
| title | Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals |
| title_full | Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals |
| title_fullStr | Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals |
| title_full_unstemmed | Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals |
| title_short | Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals |
| title_sort | underdetermined blind source separation with variational mode decomposition for compound roller bearing fault signals |
| topic | roller bearing fault diagnosis variational mode decomposition independent component analysis |
| url | http://www.mdpi.com/1424-8220/16/6/897 |
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