A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing

Variational mode decomposition denoising combined the detrended fluctuation analysis

Adaptive variational mode decomposition method for signal processing based on mode characteristic

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.

Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition.

Initial center frequency-guided VMD for fault diagnosis of rotating machines

EMD interval thresholding denoising based on similarity measure to select relevant modes

Geomagnetic matching (GM) is a new developmental technology in recent years. For resolving the cumulate errors of position, velocity, attitude in an inertial navigation system (INS), utilizing artificial fish swarm algorithm (AFSA) to carry on GM is proposed, and then the attained matching position regarded as measurement of filter to achieve the emendation of INS. Firstly, affine transformation model between inertial and real trajectories is displayed and the principle of INS/GM is explained. Secondly, the state of artificial fish (AF), distance and food consistence are defined afresh, and then the flow chart based on AFSA is given. Lastly, simulation analysis is performed in actual geomagnetic reference map (GRM). The results show that the algorithm actualizes the GM when the inertial system exist position, velocity and attitude error, that is the algorithm got the optimized global solution, with that, the data fusing is finished with the results that not only reduce error evidently but also verify the validity and feasible of the algorithm.

Simulation Research on GM Navigation Based on AFSA

To weaken the noise disturbance of GRM and improve the matching precision and matching probability of inertial/geomagnetic system, this paper proposed a method for denoising based on SVD. Firstly, from the perspective of information entropy, the singular entropy is introduced and the inner link between singular entropy and signal-to-noise ratio (SNR) is analyzed. Secondly, the method based on the asymptotic characteristic of the probabilities associated with the different singular values order (SVO) is proposed. Lastly, by utilizing practical GRM, the denoising analysis about the proposed method is demonstrated and later simulation experiments of GMN are accomplished. Simulation results show that the method is feasible and reliable.

Yuan Yuan Liu

Papers

Variational mode decomposition denoising combined the detrended fluctuation analysis

EMD interval thresholding denoising based on similarity measure to select relevant modes

Simulation Research on GM Navigation Based on AFSA

Geomagnetic Reference Map Denoising Based on Singular Entropy