Snow Chain Detection via Wheel Vibration Phase Comparison
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Solution Overview
Problem
Existing methods for detecting snow chains on vehicle wheels, such as those using autocorrelation functions, require significant computational resources and deliver unsatisfactory results with inharmonic signals.
Innovation Solution
A vibration approximation method is used to compare the movement of a driven wheel with a non-driven wheel to infer the presence of a snow chain by analyzing differences in amplitude and frequency of the vibrations, utilizing existing wheel speed sensors and requiring minimal computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autocorrelation functions are used to detect snow chains, then detection capability is provided, but computational effort and resource requirements increase significantly
Solution Approach 1:
The patent extracts only the essential features needed for snow chain detection by comparing wheel vibrations at different phases (0° and 180°) rather than performing full autocorrelation analysis. This extraction approach maintains detection reliability while significantly reducing computational complexity by focusing only on the critical vibration phase differences caused by snow chains.
Solution Approach 2:
Instead of using the conventional autocorrelation method that processes the entire signal, the patent inverts the approach by directly comparing vibration amplitudes at specific phase positions (0° and 180°). This inversion simplifies the computational process while preserving the ability to detect snow chains through the characteristic vibration patterns they produce.
2Reliability
If autocorrelation functions are used to detect snow chains, then detection is performed, but results are unsatisfactory for strongly inharmonic signals
Solution Approach 1:
The patent applies local quality by analyzing vibrations at specific critical phases (0° and 180°) rather than attempting to analyze the entire complex signal. This localized approach at key phase positions provides more reliable detection for inharmonic signals, as snow chains produce characteristic vibration differences at these specific phases that are more detectable than in the full signal spectrum.
Solution Approach 2:
The patent changes the analysis parameter from full autocorrelation to direct amplitude comparison at specific phases. This parameter change makes the detection method more robust for inharmonic signals by focusing on the fundamental vibration phase differences caused by snow chains, which remain detectable even when the overall signal is strongly inharmonic.
3Productivity
If vibration approximation is used instead of autocorrelation, then computational load is reduced, but detection accuracy must be maintained
Solution Approach 1:
The patent extracts only the critical vibration phase information at 0° and 180° positions, discarding the computationally intensive full autocorrelation process. This extraction maintains detection accuracy by focusing on the essential phase differences caused by snow chains while achieving the computational efficiency needed for real-time vehicle systems.
Solution Approach 2:
The patent inverts the conventional approach by directly comparing vibration amplitudes at specific phases rather than performing full signal processing. This inversion achieves both computational efficiency and maintained detection accuracy by using a simpler method that directly targets the snow chain-induced vibration characteristics.
Data Source
AI summary
The method involves carrying out an oscillation-approximation by the measured movement of the driven wheel and the determined oscillation is compared with an oscillation in comparison to amplitude and frequency. The oscillation is determined analog for a wheel equipped with safety not with a snow chain. The wheel, which is to be tested, and the wheel equipped with safety not with a snow chain is locked on the availability of a snow chain from significant difference between both the oscillations.