A Dimensionality Reduction Processing Method for High-Dimensional Vibration Signals

Abstract

The invention discloses a dimension reduction processing method of high-dimensional vibration signals. According to the method, a neighbor matrix of the signals is obtained by calculating the Euclidean distance between the signals, a reconfiguration weight matrix of the signals is obtained according to the neighbor matrix of the signals and by utilizing sparse constraint conditions, finally the vibration signals after dimension reduction are obtained by utilizing the reconfiguration weight matrix of the signals, and the dimension reduction process is simple. In the process of obtaining the reconfiguration weight matrix of the signals by utilizing the sparse constraint conditions, L1 norms are introduced into the sparse constraint conditions, so that the reconfiguration weight matrix has good sparseness. Accordingly, influence of noise points is effectively eliminated, anti-noise capacity is improved, and robustness of the method is ensured. Final obtaining of the vibration signals after dimension reduction is to solve a feature vector of the sparse, symmetrical and semi-positive definite matrix, and therefore calculation complexity of the method can be lowered.

Description

technical field [0001] The invention relates to a vibration signal processing method, in particular to a dimensionality reduction processing method of a high-dimensional vibration signal. Background technique [0002] With the rapid development of science and technology, high-dimensional data that is difficult to be understood, represented and processed exists in various fields, and there are many difficulties in the processing of high-dimensional data. Therefore, it is necessary to reduce high-dimensional data to low-dimensional space for follow-up processing, but the premise is to retain the original characteristic parameters of the high-dimensional data, that is, to extract these characteristic parameters. For vibration signals, the traditional feature extraction methods are mainly time-domain method and frequency-domain method. Time-domain methods include ITD method, STD method, Prony method, random decrement method, ARMA model method, ERA method, SSI method, PolyMAX me...

AI Technical Summary

Problems solved by technology

However, for high-dimensional vibration signals, using these traditional feature extraction methods to extract feature parameters often does not achieve ideal results, especially in the case of strong noise

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