Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT
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TLDR
In this article, an improved variational mode decomposition (VMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs).Abstract:Â
The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.read more
Citations
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Journal ArticleDOI
Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD
TL;DR: In this article, a novel fault diagnosis method based on variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD) was proposed for rolling elements of rolling bearings.
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An improved variational mode decomposition method based on particle swarm optimization for leak detection of liquid pipelines
Diao Xu,Diao Xu,Juncheng Jiang,Shen Guodong,Chi Zhaozhao,Zhirong Wang,Lei Ni,Ahmed Mebarki,Ahmed Mebarki,Haitao Bian,Yongmei Hao +10 more
TL;DR: The results show that the proposed PSO-VMD method is capable of de-noising background noise and appears to be efficient since the classification accuracy of the SVM method reaches up to 100% in identifying the size of the leak.
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Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks
TL;DR: This paper focuses on the possibility of detecting permanent magnet synchronous motors by analysing mechanical vibrations supported by shallow neural networks, and compared the effectiveness of the analysed NN structures from the point of view of the influence of the network architecture and various parameters of the learning process.
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Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN
TL;DR: Combining with singular value decomposition (SVD), fault eigenvalues are extracted and, in this article, fault classification is realized by K-nearest neighbor (KNN).
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Two level de-noising algorithm for early detection of bearing fault using wavelet transform and zero frequency filter
TL;DR: A zero frequency filter and wavelet transform based two level de-noising algorithm is proposed for the identification of periodic impulses in vibration signal of rolling element bearings.
References
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