Tire Vibration Analysis for Road Surface Detection
Solution Overview
Problem
Conventional methods for determining road surface conditions using time-series waveforms of tire vibration are computationally intensive due to time-stretching/contraction requirements, making it difficult to compare waveforms and resulting in increased processing time and complexity.
Innovation Solution
The method employs empirical mode decomposition to acquire natural vibration modes, followed by Hilbert transform to calculate feature data and statistic amounts like average, standard deviation, and skewness, which are used to determine road surface conditions using a Gaussian kernel function and discriminant functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If time-stretching/contraction is applied to compare vibration waveforms at different speeds, then waveform comparability is improved, but computational load and processing time increase significantly
Solution Approach 1:
The patent transforms the time-domain waveform comparison problem into a frequency-domain analysis by applying Fast Fourier Transform (FFT). This parameter transformation allows direct comparison of spectral features without time-stretching/contraction, resolving the contradiction between comparison accuracy and processing speed.
Solution Approach 2:
The patent replaces the mechanical time-warping operation with a mathematical frequency-domain transformation. Instead of physically stretching or contracting the time axis, the system uses FFT to convert waveforms into frequency spectra, enabling speed-invariant comparison through spectral feature extraction.
2Measurement precision
If time-stretching/contraction is applied to normalize waveforms, then measurement accuracy is improved, but the complexity of processing increases
Solution Approach 1:
The patent substitutes the complex time-warping mechanism with a straightforward frequency transformation using FFT. This replacement simplifies the processing pipeline by eliminating the need for iterative time-stretching algorithms while maintaining or improving measurement accuracy through spectral analysis.
Solution Approach 2:
The patent changes the analysis domain from time to frequency, transforming complex time-domain normalization operations into simpler frequency-domain feature extraction. This parameter transformation reduces processing complexity while preserving the ability to accurately distinguish road surface conditions.
3Measurement precision
If more data points are collected at higher sampling rates, then measurement precision is improved, but the amount of computation required increases
Solution Approach 1:
The patent extracts only the essential frequency components from the full vibration waveform using FFT. Instead of processing all time-domain data points, the system identifies and analyzes specific spectral features (peak frequencies, amplitude distributions), significantly reducing the effective data volume while maintaining measurement precision.
Solution Approach 2:
The patent transforms the high-volume time-domain data into a compact frequency-domain representation. This parameter transformation condenses large amounts of temporal information into a smaller set of meaningful spectral features, reducing computational requirements while preserving detection accuracy.
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
This approach significantly reduces computational load and enables quick and accurate determination of road surface conditions, improving accuracy by approximately 3% compared to conventional methods while reducing learning time.
Implementation Method 1
a vibration detecting means that is attached to the tire and that detects a time-varying waveform of vibration of the running tire
Data Source
AI summary
A method of determining a road surface condition includes: acquiring, when determining a condition of a road surface being in contact with a tire from a time-varying waveform of vibration of the running tire, the time-varying waveform of vibration having been detected by a vibration detecting means, a plurality of intrinsic vibration modes, from data of the time-varying waveform of vibration of the tire, using an algorithm of empirical mode decomposition; selecting an arbitrary intrinsic vibration mode from the plurality of intrinsic vibration modes; calculating a statistic amount from the distribution of feature data calculated by performing Hilbert transform on the selected intrinsic vibration mode to set the statistic amount as a feature amount; and determining the road surface condition from the feature amount and a feature amount obtained in advance for each road surface condition.