Autoregressive Spectral Compression for Time-Dependent Signals
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Solution Overview
Problem
Current data compression methods for time-dependent signals, particularly in continuous monitoring systems, result in low compression ratios, leading to high storage and transmission requirements, necessitating improved techniques to reduce memory and bandwidth usage.
Innovation Solution
A method involving the use of an autoregressive model to determine coefficients for compressing data spectra, where the number of coefficients is adjusted based on the state of health of the technical system, allowing for adaptive compression ratios, and incorporating timestamps for indexing and processing of amplitudes, with options for determining coefficients using AIC or BIC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If standard compression methods (ZIP, memory-optimized coding) are used on Fourier transformation data, then data can be compressed, but the compression ratio is comparatively low resulting in high storage and transmission requirements
Solution Approach 1:
The patent transforms the data representation from time-domain signals to frequency-domain spectra via Fourier transformation, then applies autoregressive modeling to represent spectral amplitudes. This parameter transformation enables much higher compression ratios because the autoregressive model can efficiently capture correlations in the frequency domain that are not apparent in the time domain
Solution Approach 2:
The patent replaces traditional mechanical compression approaches (ZIP, run-length encoding) with a statistical modeling approach using autoregressive models. Instead of applying generic compression algorithms, the system uses domain-specific statistical properties of spectral data to achieve superior compression while maintaining reconstruction accuracy
2Measurement precision
If more coefficients of the autoregressive model are used, then the accuracy of spectrum reproduction improves, but the data volume increases reducing compression efficiency
Solution Approach 1:
The patent applies partial action by using only the necessary number of autoregressive coefficients required to achieve acceptable reconstruction accuracy. Instead of using all available coefficients, the system determines the optimal subset that provides sufficient precision while maintaining high compression ratios, applying just enough modeling complexity to solve the problem
Solution Approach 2:
The patent makes the number of coefficients dynamic rather than fixed. The system can adapt the number of coefficients used in the autoregressive model based on the specific characteristics of the input signal and the desired compression ratio, allowing flexibility to optimize between accuracy and compression efficiency for different operating conditions
3Productivity
If adaptive compression based on system health state is implemented, then compression efficiency is maximized for different operating conditions, but the complexity of determining when to apply different compression levels increases
Solution Approach 1:
The patent implements feedback by monitoring system health indicators and using this information to adaptively adjust compression parameters. The system continuously evaluates the state of the monitored system and modifies the compression strategy accordingly, creating a closed-loop control that optimizes compression efficiency based on actual operating conditions
Solution Approach 2:
The system performs self-service by automatically determining the appropriate compression level based on its own monitoring of system health states. Instead of requiring external control or manual configuration, the compression system autonomously adapts its parameters based on the observed system state, reducing the need for complex external control mechanisms
Data Source
AI summary
Apparatus and a method for compressing data that represent a time-dependent signal that includes a multiplicity of time-dependent signal elements, wherein a multiplicity of spectra are received, where each spectrum corresponds to one of the time-dependent signal elements, and where each spectrum includes a multiplicity of frequencies fj and a multiplicity of amplitudes of the multiplicity of frequencies, wherein a compressed data record is generated, wherein a respective number of coefficients of an autoregressive model for the multiplicity of amplitudes of each of the multiplicity of frequencies is ascertained, and wherein the compressed data record is generated, where the compressed data record includes at least the number of coefficients and the frequencies associated with the coefficients.