Endpoint Effect Suppression Method Based on Intrinsic Waveform Matching

Abstract

The method discloses an end effect suppression method based on intrinsic waveform matching, which belongs to the field of signal analysis and fault diagnosis, in particular an end effect suppression method based on waveform matching. In this invention, the two ends of the waveform are respectively extended. First, the central trend is eliminated for the received waveform, and then a segment of the waveform at the end is selected to match with other segment waveforms in the waveform where the central trend is eliminated; if the matching is successful, the successful matching is selected. This segment of the original waveform is extended to the received waveform, and if the matching is unsuccessful, the segment of the waveform at the end is used for extension. Therefore, it has the effect of high accuracy and good stability.

Description

technical field [0001] The invention belongs to the field of signal analysis and fault diagnosis, in particular to an endpoint effect suppression method based on waveform matching. Background technique [0002] The phenomenon of distortion or divergence at both ends of the IMF waveform is called the endpoint effect, also known as the endpoint flying wing such as figure 1 shown. The endpoint effect may cause the following problems: (1) The endpoint effect will continue to diffuse into the signal during the screening process, and the IMF that is subsequently screened out will have more serious endpoint effects; (2) The endpoint effect will cause false modes to appear in the decomposition results; (3) The endpoint effect makes the data near the endpoint lose its analytical value, and discarding some data at the endpoint will reduce the integrity of the analysis data. In practical applications, if the data sequence of the signal is relatively long, the usual practice is to con...

AI Technical Summary

Problems solved by technology

The endpoint effect may cause the following problems: (1) The endpoint effect will continue to spread into the signal during the screening process, and the subsequent IMF screened out will have more severe endpoint effects; (2) The endpoint effect will cause false modes to appear in the decomposition results; (3) The endpoint effect makes the data near the endpoint lose its analytical value, and discarding some data at the endpoint will reduce the integrity of the analysis data

[0004] The algorithm proposed by Shao Chenxi uses a constant length relative to the reference point to determine the starting point of the matching wavelet, which may lead to erroneous matching results

like figure 2 As shown, according to the matching error calculation formula, the two wavelets are matched, but the right end point of the characteristic wavelet is the extreme value, but the right end point of the matching wavelet is not the extreme value, obviously at this time the matching wavelet is regarded as the best matching waves is inappropriate

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