Spectral Signal Compression with Noise-Floor Bitmap Encoding
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Solution Overview
Problem
Generic data compression techniques fail to achieve optimal compression of spectral data, particularly in applications where steady-state operations dominate, leading to inefficiencies in data storage and transmission, and existing methods often sacrifice compression quality for consistency.
Innovation Solution
A method involving thresholding spectral data to remove noise floor values, encoding non-zero values as a bitmap and a separate dataword, and applying non-linear quantization to reduce data size, while allowing for efficient reconstruction by retaining peak information and discarding redundant data, especially suitable for monitoring equipment like engines and generators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If generic data compression techniques are used, then data transmission bandwidth is reduced, but compression ratio is insufficient for spectral data
Solution Approach 1:
The patent applies parameter changes by transforming spectral data from time-domain to frequency-domain representation, then applying thresholding based on noise floor parameters. This transformation allows the system to identify and compress only significant spectral components, achieving superior compression ratios while maintaining signal integrity for steady-state operations.
Solution Approach 2:
The patent implements local quality by differentiating between significant and insignificant spectral components using noise floor thresholding. Only components above the noise floor are retained and encoded, while below-threshold components are discarded. This selective approach optimizes compression by focusing resources on preserving meaningful signal information rather than uniformly processing all data points.
2Reliability
If lossless compression is used, then perfect reconstruction is possible, but compression ratio is reduced
Solution Approach 1:
The patent extracts and removes noise floor components from the spectral data before compression. By separating significant signal components from noise, the system can apply lossy compression to the noise portion (discarding it) while preserving critical signal information. This extraction approach enables higher compression ratios without significantly impacting the reconstruction of meaningful signal content.
Solution Approach 2:
The patent applies partial action by selectively compressing only the significant spectral components above the noise floor, rather than attempting to preserve all data points with equal fidelity. This partial preservation strategy achieves practical reconstruction accuracy for steady-state monitoring while dramatically improving compression ratios compared to lossless methods that would preserve all components.
3Loss of information
If all spectral data is retained, then complete signal information is preserved, but data size is large
Solution Approach 1:
The patent changes the representation parameters of spectral data by applying frequency-domain transformation and noise floor-based thresholding. This parameter transformation converts the data from a dense time-domain representation to a sparse frequency-domain representation, where only significant spectral components are retained, thereby reducing data size while preserving essential signal characteristics.
Solution Approach 2:
The patent extracts and removes redundant noise floor components from the spectral data. By identifying and eliminating these insignificant components through thresholding, the system reduces data size by discarding approximately 95% or more of the original data points while retaining the critical peak information necessary for steady-state operation monitoring.
4Productivity
If compression focuses on steady-state operations, then compression ratio improves, but transient event compression deteriorates
Solution Approach 1:
The patent implements dynamics by using adaptive thresholding that can adjust to changing signal conditions. The noise floor threshold is determined dynamically from the signal characteristics, allowing the compression algorithm to adapt to both steady-state and transient conditions. This dynamic approach ensures that significant transient events above the adaptive threshold are preserved while still achieving high compression during steady-state operations.
Data Source
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.