The Experts below are selected from a list of 7959 Experts worldwide ranked by ideXlab platform
Kun Feng - One of the best experts on this subject based on the ideXlab platform.
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Research on variational mode decomposition in Rolling Bearings fault diagnosis of the multistage centrifugal pump
Mechanical Systems and Signal Processing, 2017Co-Authors: Ming Zhang, Zhinong Jiang, Kun FengAbstract:Rolling bearing faults are among the primary causes of breakdown in multistage centrifugal pump. A novel method of Rolling Bearings fault diagnosis based on variational mode decomposition is presented in this contribution. The Rolling bearing fault signal calculating model of different location defect is established by failure mechanism analysis, and the simulation vibration signal of the proposed fault model is investigated by FFT and envelope analysis. A comparison has gone to evaluate the performance of bearing defect characteristic extraction for Rolling Bearings simulation signal by using VMD and EMD. The result of comparison verifies the VMD can accurately extract the principal mode of bearing fault signal, and it better than EMD in bearing defect characteristic extraction. The VMD is then applied to detect different location fault features for Rolling Bearings fault diagnosis via modeling simulation vibration signal and practical vibration signal. The analysis result of simulation and experiment proves that the proposed method can successfully diagnosis Rolling Bearings fault.
Ming Zhang - One of the best experts on this subject based on the ideXlab platform.
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Research on variational mode decomposition in Rolling Bearings fault diagnosis of the multistage centrifugal pump
Mechanical Systems and Signal Processing, 2017Co-Authors: Ming Zhang, Zhinong Jiang, Kun FengAbstract:Rolling bearing faults are among the primary causes of breakdown in multistage centrifugal pump. A novel method of Rolling Bearings fault diagnosis based on variational mode decomposition is presented in this contribution. The Rolling bearing fault signal calculating model of different location defect is established by failure mechanism analysis, and the simulation vibration signal of the proposed fault model is investigated by FFT and envelope analysis. A comparison has gone to evaluate the performance of bearing defect characteristic extraction for Rolling Bearings simulation signal by using VMD and EMD. The result of comparison verifies the VMD can accurately extract the principal mode of bearing fault signal, and it better than EMD in bearing defect characteristic extraction. The VMD is then applied to detect different location fault features for Rolling Bearings fault diagnosis via modeling simulation vibration signal and practical vibration signal. The analysis result of simulation and experiment proves that the proposed method can successfully diagnosis Rolling Bearings fault.
Deng Linfeng - One of the best experts on this subject based on the ideXlab platform.
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Fault Feature Extraction of Rolling Bearings Based on Variational Mode Decomposition and Singular Value Entropy
DEStech Transactions on Computer Science and Engineering, 2017Co-Authors: Chen Zhang, Rongzhen Zhao, Deng LinfengAbstract:In order to solve the problem that the fault characteristic signals of Rolling Bearings are weak and the fault identification is relatively difficult, a fault feature extraction method for Rolling Bearings based on variational mode decomposition singular value entropy is proposed. The original signals are decomposed by variational mode decomposition, and some intrinsic modal functions are obtained to form the initial characteristic matrix. Then, the singular value decomposition technique is used to process the initial characteristic matrix and the singular value entropy is obtained by combining the information entropy theory. Finally, according to the magnitude of the singular value entropy, the working states and fault types of Rolling Bearings are distinguished. The results show that this method can classify the weak faults of Rolling Bearings more clearly and has higher fault identification accuracy.
Zhinong Jiang - One of the best experts on this subject based on the ideXlab platform.
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Research on variational mode decomposition in Rolling Bearings fault diagnosis of the multistage centrifugal pump
Mechanical Systems and Signal Processing, 2017Co-Authors: Ming Zhang, Zhinong Jiang, Kun FengAbstract:Rolling bearing faults are among the primary causes of breakdown in multistage centrifugal pump. A novel method of Rolling Bearings fault diagnosis based on variational mode decomposition is presented in this contribution. The Rolling bearing fault signal calculating model of different location defect is established by failure mechanism analysis, and the simulation vibration signal of the proposed fault model is investigated by FFT and envelope analysis. A comparison has gone to evaluate the performance of bearing defect characteristic extraction for Rolling Bearings simulation signal by using VMD and EMD. The result of comparison verifies the VMD can accurately extract the principal mode of bearing fault signal, and it better than EMD in bearing defect characteristic extraction. The VMD is then applied to detect different location fault features for Rolling Bearings fault diagnosis via modeling simulation vibration signal and practical vibration signal. The analysis result of simulation and experiment proves that the proposed method can successfully diagnosis Rolling Bearings fault.
Chen Zhang - One of the best experts on this subject based on the ideXlab platform.
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Fault Feature Extraction of Rolling Bearings Based on Variational Mode Decomposition and Singular Value Entropy
DEStech Transactions on Computer Science and Engineering, 2017Co-Authors: Chen Zhang, Rongzhen Zhao, Deng LinfengAbstract:In order to solve the problem that the fault characteristic signals of Rolling Bearings are weak and the fault identification is relatively difficult, a fault feature extraction method for Rolling Bearings based on variational mode decomposition singular value entropy is proposed. The original signals are decomposed by variational mode decomposition, and some intrinsic modal functions are obtained to form the initial characteristic matrix. Then, the singular value decomposition technique is used to process the initial characteristic matrix and the singular value entropy is obtained by combining the information entropy theory. Finally, according to the magnitude of the singular value entropy, the working states and fault types of Rolling Bearings are distinguished. The results show that this method can classify the weak faults of Rolling Bearings more clearly and has higher fault identification accuracy.
