Spectral Data Compression Using Autoregressive Coefficients
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Solution Overview
Problem
Current methods for compressing data representing time-dependent signals, such as those from machine vibration monitoring, require significant memory space and bandwidth, with low compression ratios, necessitating improved techniques to efficiently manage and transmit large volumes of data.
Innovation Solution
A method that involves receiving multiple spectra corresponding to time-dependent signal elements, determining autoregressive model coefficients for the amplitudes of these spectra, and generating a compressed data record, with the number of coefficients adjusted based on the state of health of the technical system to optimize compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If standard compression tools (ZIP) or memory-optimized coding are used, then data can be compressed, but the compression ratio is comparatively low and significant memory space is required
Solution Approach 1:
The patent transforms the data representation from time-domain signals to frequency-domain spectra, and further compresses by representing spectral amplitudes through autoregressive model coefficients. This parameter transformation enables much higher compression ratios while maintaining data integrity, directly resolving the contradiction between memory space reduction and compression effectiveness
Solution Approach 2:
The patent replaces traditional mechanical compression algorithms (like ZIP) with a mathematical modeling approach using autoregressive models. This substitution allows the system to achieve superior compression ratios by exploiting the statistical properties of spectral data rather than relying on generic compression mechanisms
2Measurement precision
If a large number of coefficients are used for accurate data representation, then measurement precision is maintained, but memory space requirements increase
Solution Approach 1:
The patent applies partial action by selectively determining coefficients only for frequencies that exceed a threshold value. Instead of computing all possible coefficients, the method focuses computational resources on significant frequency components, thereby maintaining measurement precision for important data while reducing overall memory requirements
Solution Approach 2:
The patent implements local quality by applying different processing strategies to different frequency components. Frequencies above the threshold receive full autoregressive modeling with multiple coefficients, while frequencies below the threshold are handled differently or discarded, optimizing the balance between precision and memory usage for each local region of the spectrum
Data Source
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AI summary
The invention relates to a method for compressing data D (nT) (65) that represent a time-dependent signal A(t) (45). The time-dependent signal A(t) (45) comprises a multiplicity of time-dependent signal elements Ai(t) (where i=1,..., NoS) (46-50). The method comprises the steps of: reception of a multiplicity of spectra Si(f) (where i=1,..., NoS) (106-110), wherein each spectrum Si(f) (106-110) corresponds to one of the time-dependent signal elements Ai(t) (46-50), wherein each spectrum Si(f) (106-110) comprises a multiplicity of frequencies fj (where j=1,, NoP) (111-116) and a multiplicity of amplitudes aj,i (where j=1,..., NoP and i=1,..., NoS) (121-126) of the multiplicity of frequencies fj (111-116); generation of a compressed data record (CDS); ascertainment of a respective number of coefficients (ARC) of an autoregressive model for the multiplicity of amplitudes aj,i (121-126) of each of the multiplicity of frequencies fj (111-116); and generation of the compressed data record (CDS), wherein the compressed data record (CDS) comprises at least the number of coefficients (ARC) and the frequencies fj(111-116) associated with the coefficients (ARC).