A Vibration Signal Data Compression Acquisition Method Based on Deterministic Measurement Matrix

Abstract

The invention discloses a data compression acquisition method for a vibration signal based on a deterministic measurement matrix, comprising the steps of: extracting an acquired vibration signal, performing sparsity analysis under a DCT (Discrete Cosine Transform) orthogonal basis to obtain a sparsity degree K; building a first orthogonal symmetric deterministic measurement matrix based on the deterministic measurement matrix according to the sparsity degree K; performing iteration on the obtained first orthogonal symmetric deterministic measurement matrix through adoption of a threshold valueiterative shrinkage algorithm to obtain a second orthogonal symmetric deterministic measurement matrix; and performing optimization on the obtained second orthogonal symmetric deterministic measurement matrix through adoption of a singular value decomposition algorithm to obtain a third orthogonal symmetric deterministic measurement matrix. The data compression acquisition method of the inventionsolves the problem that the deterministic measurement matrix is not widely applied due to a large rebuilding error thereof.

Description

technical field [0001] The invention belongs to the field of data compression and acquisition, in particular to a vibration signal data compression and acquisition method based on a deterministic measurement matrix. Background technique [0002] The currently widely used vibration signal acquisition is the analog-to-digital (A / D) sampling guided by the Nyquist sampling theory, but the sampling frequency is not lower than twice the highest frequency of the original signal in order to accurately reconstruct the signal . However, today's mechanical equipment is becoming increasingly high-speed and large-scale, and the vibration frequency is getting higher and higher, showing nonlinear and non-stationary characteristics. If the traditional sampling theorem is still used for sampling, a higher sampling frequency will be required, and a large amount of monitoring data will be generated at the same time. The real-time transmission, synchronous storage and post-processing of these ...

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