In the context of signal processing, the comparison of time histories is required for different purposes, especially for the model validation of vehicle safety. Most of the existing metrics focus on the mathematical value only. Therefore, they suffer the measuring errors, disturbance, and uncertainties and can hardly achieve a stable result with a clear physical interpretation. This paper proposes a novel scheme of time histories comparison to be used in vehicle safety analysis. More specifically, each signal for comparison is decomposed into a trend signal and several intrinsic mode functions (IMFs) by ensemble empirical mode decomposition. The trend signals reflect the general variation and are free from the influence of high-frequency disturbances. With the help of dynamic time warping, the errors of time and magnitude between trends are calculated. The IMFs, which contain high-frequency information, are compared on frequency, magnitude, and local features. To illustrate the full scope and effectiveness of the proposed scheme, this paper provides three vehicle crash cases.
An EEMD aided comparison of time histories and its application in vehicle safety
Karimi, Hamid Reza
2017-01-01
Abstract
In the context of signal processing, the comparison of time histories is required for different purposes, especially for the model validation of vehicle safety. Most of the existing metrics focus on the mathematical value only. Therefore, they suffer the measuring errors, disturbance, and uncertainties and can hardly achieve a stable result with a clear physical interpretation. This paper proposes a novel scheme of time histories comparison to be used in vehicle safety analysis. More specifically, each signal for comparison is decomposed into a trend signal and several intrinsic mode functions (IMFs) by ensemble empirical mode decomposition. The trend signals reflect the general variation and are free from the influence of high-frequency disturbances. With the help of dynamic time warping, the errors of time and magnitude between trends are calculated. The IMFs, which contain high-frequency information, are compared on frequency, magnitude, and local features. To illustrate the full scope and effectiveness of the proposed scheme, this paper provides three vehicle crash cases.File | Dimensione | Formato | |
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