Vehicle Modal Parameter Estimation via Correlated Road Input Modeling
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Solution Overview
Problem
Current Operational Modal Analysis (OMA) methods fail to accurately estimate modal parameters of vehicle systems under working conditions due to non-stationary loads, and existing methods for characterizing road/rail surface roughness do not account for input correlation, leading to errors and the introduction of spurious modes.
Innovation Solution
A method that estimates modal parameters and characterizes road/rail surface roughness by analyzing displacements, velocities, or accelerations from vehicles in motion, considering the vehicle's geometry and the correlation of inputs, using a mathematical model that accounts for colored noise and spatial/temporal correlations, reducing the number of quantities needed to characterize parallel profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OMA methods are used to estimate modal parameters, then the analysis is simple, but the estimation is inaccurate when NExT hypotheses are not satisfied
Solution Approach 1:
The patent changes the fundamental parameters of the OMA method by incorporating a mathematical model that accounts for colored noise and spatial/temporal correlations of road inputs. This transforms the traditional white noise assumption into a colored noise model with correlation structures, enabling accurate modal parameter estimation under non-NExT conditions while maintaining methodological coherence
Solution Approach 2:
The patent introduces a mathematical model as an intermediary between the road inputs and the vehicle response analysis. This model acts as a mediator that captures the complex correlation structures of road inputs, allowing the OMA method to accurately estimate modal parameters without requiring white noise excitation or uncorrelated inputs
2Measurement precision
If traditional methods characterize road surface roughness, then direct measurements are used, but input correlation is not accounted for leading to errors
Solution Approach 1:
The patent uses the vehicle's dynamic response as an intermediary to indirectly characterize road surface roughness. Instead of directly measuring road surfaces and ignoring correlations, the method extracts road input characteristics from vehicle responses through a mathematical model that explicitly accounts for spatial and temporal correlations, thereby achieving accurate road surface characterization
Solution Approach 2:
The patent replaces direct mechanical measurement of road surfaces with an indirect method based on vehicle dynamic response analysis. By substituting direct profilometer measurements with vibration-based indirect characterization, the method captures correlation effects that direct measurements miss, improving accuracy while reducing measurement complexity
3Reliability
If the vehicle moves on correlated inputs, then the inputs cannot be traced back to white noises, but traditional OMA methods fail to accurately estimate modal parameters
Solution Approach 1:
The patent fundamentally changes the noise model parameters from white noise to colored noise with specific correlation structures. By modeling the road inputs as colored noise with spatial and temporal correlations that reflect actual driving conditions, the method achieves reliable modal parameter estimation while being adaptable to various working conditions and road types
Solution Approach 2:
The patent introduces dynamic correlation structures into the OMA framework by modeling how road inputs correlate in space and time as the vehicle moves. This dynamic approach allows the method to adapt to varying driving conditions, speeds, and road characteristics, maintaining reliability across different operational scenarios
Data Source
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AI summary
Method for the identification of poles ( λ n ) and modal vectors ( ψ n ) of a road or rail vehicle provided with at least two wheels and in working condition, by means of the analysis of the movements or speeds or accelerations (output of the system) acquired in assigned measuring points of said vehicle, wherein said poles and modal vectors are determined by means of the fitting of the data relating to said outputs of the system on the basis of a mathematical model which describes the interaction between road or railway and said vehicle, characterized by hypothesizing that said vehicle moves at constant speed on a rectilinear trajectory or bend with constant radius, hypothesizing that said vehicle moves on a homogeneous and ergodic surface, whose roughness has a Gaussian distribution and that said at least two wheels move on a profile or on a plurality of profiles parallel with respect to each other, hypothesizing that the inlets induced by the road or rail surface on said vehicle cannot be traced back to a sequence of white noises and are correlated with respect to each other in time and/or space.