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Abstract
Given the problem that the low sampling period of the servo controller cannot provide high-frequency information for high-precision servo control system state recognition, this paper proposes a high-resolution signal reconstruction method based on sparse structure preservation. The servo system status data is reconstructed to obtain high sampling rate data equivalent to direct measurement, which provides support for extracting system features and status recognition. The main research content of this paper includes verifying the sparseness of the servo control signal, analyzing the consistency of the sparse structure of different sampling rates signals; extracting characteristics based on the combination of empirical mode decomposition (EMD) and principal component analysis (PCA) method. An adaptive sparse dictionary for servo control signals is trained by K-SVD. An objective function is constructed for high-resolution signal reconstruction based on the sparse structure retention properties. It is proved by simulations and experiments that the high-resolution reconstructed signal can be obtained, which is consistent with the high-resolution signal obtained by direct measurement. The method can be used as a reference for the analysis of low-sampling signals of servo control systems of industrial robots and similar equipment and has certain engineering application value.
Highlights
With the increase in labor costs and the development of manufacturing, industrial robots play an increasingly important role and are widely used in repetitive and continuous work [1], [2]
If the resolution of the collected low-sampling signal can be improved by a certain method, that is, the high-frequency detail components lost in the signal are reconstructed, which will provide better signal characteristics for subsequent fault diagnosis and improve the accuracy of the analysis
The IMF components representing the characteristic signals of different time scales of the low-sample signal are formed into a data set, and the residual signal containing the lowest frequency component is removed to obtain the characteristic information related to the high-frequency signal; considering the impact of the increase in the amount of data on the reconstruction process, the principal component analysis (PCA) method is used to perform a data set composed of data segments dimensionality reduction processing
Summary
With the increase in labor costs and the development of manufacturing, industrial robots play an increasingly important role and are widely used in repetitive and continuous work [1], [2]. If the resolution of the collected low-sampling signal can be improved by a certain method, that is, the high-frequency detail components lost in the signal are reconstructed, which will provide better signal characteristics for subsequent fault diagnosis and improve the accuracy of the analysis. Liu et al used the prior information of the sparse structure of the signal and used the redesigned structure-aware Bayesian compressed sensing algorithm to achieve high-resolution signal reconstruction. To solve the problem that the low sampling period of the servo controller cannot provide high-frequency information for the high-precision servo control system state recognition, the method of this paper is improved based on the method proposed in the literature [12] and proposes a low Sampling servo control signal high-resolution reconstruction method. The resolution of low-sampling signals is improved based on the consistency of the sparse structure of different sparse signals, and high-resolution signals with good signal-to-noise ratio and reconstruction error are obtained. 3) The effectiveness of the proposed method was verified by simulation and experiment
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