Dequantizing Low-Resolution IoT Signals for Prognostic Indicators
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Solution Overview
Problem
Low-resolution analog-to-digital converters in IoT sensors cause quantization effects, making it difficult for modern statistical pattern-recognition techniques to detect subtle anomalies before system failures, hindering proactive fault monitoring in IoT systems.
Innovation Solution
A system that removes quantization effects from time-series signals by determining the number of quantization levels, performing FFT and inverse FFT operations, and selecting the optimal Fourier modes to produce dequantized signals, which are then used for anomaly detection and proactive fault monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If low-resolution A/D converters are used in IoT sensors, then device complexity and cost are reduced, but measurement precision and signal resolution deteriorate
Solution Approach 1:
The patent introduces an intermediary dequantization process that acts as a mediator between the low-resolution quantized signal and the high-resolution analysis requirements. The dequantization module reconstructs the original continuous signal from discrete quantized values using statistical modeling and signal processing techniques, effectively bridging the resolution gap without requiring higher-resolution hardware
Solution Approach 2:
The patent changes the parameter of signal resolution through computational processing rather than hardware improvement. By applying dequantization algorithms that model the quantization process and reverse it statistically, the system transforms low-resolution quantized signals into high-resolution reconstructed signals, effectively changing the resolution parameter software-based
2Ease of operation
If quantization effects are present in IoT signals, then data transmission and processing are simplified, but the ability to detect subtle anomalies using modern statistical pattern-recognition techniques deteriorates
Solution Approach 1:
The patent applies preliminary dequantization action to the signal before it undergoes anomaly detection processing. By reconstructing the continuous signal from quantized values in advance, the system prepares the signal in a form that is suitable for modern statistical pattern-recognition techniques, enabling them to function effectively on what would otherwise be unsuitable discrete data
3Measurement precision
If dequantization processing is applied to low-resolution signals, then signal resolution and anomaly detection accuracy are improved, but computational complexity and processing time increase
Solution Approach 1:
The patent replaces complex mechanical or hardware-based high-resolution A/D conversion with a computational dequantization system. Instead of using expensive high-resolution converters, the system uses software-based signal reconstruction algorithms that achieve equivalent or superior resolution through processing, substituting computational complexity for hardware complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution significantly improves the accuracy of signal reconstruction, enabling effective detection of incipient anomalies and impending failures in IoT systems, even with low-resolution signals, by enhancing the resolution of quantized signals by a factor of 10 to 100.
Implementation Method 1
the system performs a fast Fourier transform (FFT) on the time-series signal to produce a set of Fourier modes for the time-series signal
Implementation Method 2
The system then performs an inverse FFT operation using the Nmode largest-amplitude Fourier modes to produce a dequantized time-series signal
Data Source
AI summary
The disclosed embodiments relate to a system that removes quantization effects from a set of time-series signals to produce highly accurate approximations of a set of original unquantized signals. During operation, for each time-series signal in the set of time-series signals, the system determines a number of quantization levels (NQL) in the time-series signal. Next, the system performs a fast Fourier transform (FFT) on the time-series signal to produce a set of Fourier modes for the time-series signal. The system then determines an optimal number of Fourier modes (Nmode) to reconstruct the time-series signal based on the determined NQL for the time-series signal. Next, the system selects Nmode largest-amplitude Fourier modes from the set of Fourier modes for the time-series signal. The system then performs an inverse FFT operation using the Nmode largest-amplitude Fourier modes to produce a dequantized time-series signal to be used in place of the time-series signal.