Author
Hongliang Yin
Bio: Hongliang Yin is an academic researcher. The author has contributed to research in topics: Hilbert–Huang transform & Noise (signal processing). The author has an hindex of 1, co-authored 1 publications receiving 157 citations.
Papers
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TL;DR: A novel signal denoising method that combines variational mode decomposition (VMD) and detrended fluctuation analysis (DFA), named DFA-VMD, is proposed in this paper and shows the superior performance of this proposed filtering over EMD-based denoisings and discrete wavelet threshold filtering.
214 citations
Cited by
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TL;DR: Experimental results show that the proposed fault classification algorithm achieves high diagnosis accuracy for different working conditions of rolling bearing and outperforms some traditional methods both mentioned in this paper and published in other literature.
316 citations
TL;DR: The proposed Adaptive Variational Mode Decomposition (AVMD) method has strong adaptability and is robust to noise and can determine the mode number appropriately without modulation even when the signal frequencies are relatively close.
178 citations
TL;DR: The proposed IPAVMD outperforms the traditional parameter-adaptive VMD and further expands the application to compound-fault diagnosis and improves the optimization objective function of grasshopper optimization algorithm based on the ensemble kurtosis.
Abstract: Parameter-adaptive variational mode decomposition (VMD) has attenuated the dominant effect of prior parameters, especially the predefined mode number and balancing parameter, which heavily trouble the traditional VMD. However, parameter-adaptive VMD still encounters some problems when it is applied to the data from industry applications. On one hand, the mode number chosen using parameter-adaptive VMD is not the optimal. Numbers of redundant modes are decomposed. On another hand, parameter-adaptive VMD has much space for the improvement when it is applied to compound-fault diagnosis. To solve these issues and further enhance its performance, an improved parameter-adaptive VMD (IPAVMD) is proposed in this paper. Firstly, a new index, called ensemble kurtosis, is constructed by combining with kurtosis and the envelope spectrum kurtosis. It can simultaneously take the cyclostationary and impulsiveness into consideration. Secondly, the optimization objective function of grasshopper optimization algorithm is improved based on the ensemble kurtosis. The improved method chooses the mean value of the ensemble kurtosis of all modes rather than that of the individual mode as objective function. Thirdly, to extract all potential fault information, an iteration algorithm is used in the new method. Benefiting from these improvements, the proposed IPAVMD outperforms the traditional parameter-adaptive VMD and further expands the application to compound-fault diagnosis. It has been verified by a series of simulated signals and a real dataset from the axle box bearings of locomotive.
162 citations
TL;DR: The variation features of the center frequency (CF) of extracted modes are investigated with different ICFs, in which the converging U-shape phenomenon is found and a novel ICF-guided VMD method is proposed to extract accurately the weak damage features of rotating machines.
156 citations
TL;DR: A coarse-to-fine decomposing strategy is proposed for weak fault detection of rotating machines and can well-detect the weak repetitive transients in the signals with heavy noise and overcome the drawbacks of the original VMD.
151 citations