Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification
As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere i...
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MDPI AG
2018-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/7/2325 |
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| author | Yong Lv Houzhuang Zhang Cancan Yi |
| author_facet | Yong Lv Houzhuang Zhang Cancan Yi |
| author_sort | Yong Lv |
| collection | DOAJ |
| description | As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere is used to estimate the local mean. However, the unbalanced data distribution in high-dimensional space often conflicts with the uniform samples and its performance is sensitive to the noise components. Considering the common fact that the vibration signal is generated by three sensors located in different measuring positions in the domain of the structural health monitoring for the key equipment, thus a novel trivariate empirical mode decomposition via convex optimization was proposed for rolling bearing condition identification in this paper. For the trivariate data matrix, the low-rank matrix approximation via convex optimization was firstly conducted to achieve the denoising. It is worthy to note that the non-convex penalty function as a regularization term is introduced to enhance the performance. Moreover, the non-uniform sample scheme was determined by applying singular value decomposition (SVD) to the obtained low-rank trivariate data and then the approach used in conventional MEMD algorithm was employed to estimate the local mean. Numerical examples of synthetic defined by the fault model and real data generated by the fault rolling bearing on the experimental bench are provided to demonstrate the fruitful applications of the proposed method. |
| first_indexed | 2024-04-11T13:10:02Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:10:02Z |
| publishDate | 2018-07-01 |
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| spelling | doaj.art-1288a53f3699458199da05c4b77b264b2022-12-22T04:22:37ZengMDPI AGSensors1424-82202018-07-01187232510.3390/s18072325s18072325Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition IdentificationYong Lv0Houzhuang Zhang1Cancan Yi2Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, ChinaKey Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, ChinaKey Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, ChinaAs a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere is used to estimate the local mean. However, the unbalanced data distribution in high-dimensional space often conflicts with the uniform samples and its performance is sensitive to the noise components. Considering the common fact that the vibration signal is generated by three sensors located in different measuring positions in the domain of the structural health monitoring for the key equipment, thus a novel trivariate empirical mode decomposition via convex optimization was proposed for rolling bearing condition identification in this paper. For the trivariate data matrix, the low-rank matrix approximation via convex optimization was firstly conducted to achieve the denoising. It is worthy to note that the non-convex penalty function as a regularization term is introduced to enhance the performance. Moreover, the non-uniform sample scheme was determined by applying singular value decomposition (SVD) to the obtained low-rank trivariate data and then the approach used in conventional MEMD algorithm was employed to estimate the local mean. Numerical examples of synthetic defined by the fault model and real data generated by the fault rolling bearing on the experimental bench are provided to demonstrate the fruitful applications of the proposed method.http://www.mdpi.com/1424-8220/18/7/2325trivariate empirical mode decompositionconvex optimizationlow-rank matrix approximationrolling bearing condition identification |
| spellingShingle | Yong Lv Houzhuang Zhang Cancan Yi Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification Sensors trivariate empirical mode decomposition convex optimization low-rank matrix approximation rolling bearing condition identification |
| title | Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification |
| title_full | Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification |
| title_fullStr | Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification |
| title_full_unstemmed | Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification |
| title_short | Trivariate Empirical Mode Decomposition via Convex Optimization for Rolling Bearing Condition Identification |
| title_sort | trivariate empirical mode decomposition via convex optimization for rolling bearing condition identification |
| topic | trivariate empirical mode decomposition convex optimization low-rank matrix approximation rolling bearing condition identification |
| url | http://www.mdpi.com/1424-8220/18/7/2325 |
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