Spectral Data Compression Using Noise-Floor Peak Encoding
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Solution Overview
Problem
Existing data compression techniques fail to achieve optimal compression of spectral data, particularly in applications where steady-state operations dominate, due to limitations in handling transient events and bandwidth constraints, and often result in inefficient storage and transmission requirements.
Innovation Solution
A method involving thresholding spectral data based on a noise floor to retain only non-zero values, encoding these values as bitmaps and second datawords, and using non-linear quantization to further compress data, while allowing for efficient reconstruction by comparing successive data sets and setting a re-use flag for similar data sets to discard redundant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic data compression techniques are used, then implementation is simple, but compression performance is suboptimal for spectral data
Solution Approach 1:
The patent transforms spectral data from time-domain to frequency-domain representation using Fourier transforms, changing the parameter domain to enable more effective compression. This domain transformation allows the compression algorithm to exploit spectral characteristics and achieve superior compression ratios while maintaining signal fidelity.
Solution Approach 2:
The patent applies different processing strategies to different regions of the spectral data based on local characteristics. Significant spectral components are preserved with high fidelity while less important regions are compressed more aggressively, optimizing overall compression performance while maintaining signal quality where it matters most.
2Reliability
If lossless compression is used, then perfect reconstruction is possible, but compression ratio is reduced
Solution Approach 1:
The patent applies non-uniform quantization to the spectral coefficients, transforming the parameter representation to allocate more precision to significant components and less precision to less important ones. This enables lossy compression with ratios exceeding 10:1 while maintaining sufficient signal fidelity for practical applications.
3Productivity
If steady-state compression optimization is used, then average compression is improved, but transient event compression deteriorates
Solution Approach 1:
The patent implements dynamic thresholding and adaptive quantization that adjusts processing parameters based on the detected signal state. During transient events, the system automatically increases precision and reduces compression aggression, while during steady-state operation, it optimizes for maximum compression ratio, thus handling both scenarios effectively.
Data Source
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Figure 2
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AI summary
A method and apparatus for data compression, particularly applicable to spectral signals such as Fast Fourier Transforms of vibration data. The data is merged to remove redundant frequencies when recorded at multiple sample rates, thresholded with respect to a noise floor to remove even more redundant data, and then the positions of non-zero signal values, with respect to the noise floor, are recorded in a first dataword and the non-zero signal values themselves are all recorded concatenated to form a second dataword. The compressed data set consists of the first and second datawords, together with the value of the noise floor, maximum original amplitude and the broadband power. In the event of successive data sets having the same or similar locations for non-zero signal values a re-use flag may be set and the locations dataword discarded. Preferably the signal values are non-linearly quantized to further reduce the amount of data.