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Virtual-Array Operational Modal Analysis of Rolling Tires Using a Single Tire Cavity Accelerometer

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arXiv:2606.10437v1 Announce Type: new Abstract: The dynamics of rolling tires significantly influence the low-frequency (0-500 Hz) structure-borne noise within vehicles. Accurately characterizing these dynamics under realistic operating conditions remains challenging. Current state-of-the-art methods, primarily relying on Laser Doppler Vibrometers (LDV), are complex to implement, time-intensive, and generally limited to smooth tires in laboratory environments due to issues with speckle...

arXiv:2606.10437v1 Announce Type: new Abstract: The dynamics of rolling tires significantly influence the low-frequency (0-500 Hz) structure-borne noise within vehicles. Accurately characterizing these dynamics under realistic operating conditions remains challenging. Current state-of-the-art methods, primarily relying on Laser Doppler Vibrometers (LDV), are complex to implement, time-intensive, and generally limited to smooth tires in laboratory environments due to issues with speckle formation on treaded surfaces. This study introduces an innovative strategy for Operational Modal Analysis (OMA) of a rolling tire using a single wireless Tire Cavity Accelerometer (TCA) together with two optical sensors. The methodology leverages the non-integer ratio between the tire and drum diameters in a test rig to create a virtual sensor array. By utilizing optical sensors to time-stamp the cleat impact (on the drum) precisely and the TCA position (on the tire), the vibration responses from multiple revolutions are clustered according to the TCA's circumferential position at the moment of impact. This effectively synthesizes responses from an array of virtual sensors distributed around the tire circumference using data from a single test run. The clustered signals are conditioned using order tracking to remove periodic components arising from contact patch deformation. Both Frequency Domain Decomposition (FDD) and Covariance-based Stochastic Subspace Identification (SSI-Cov) were employed for modal identification. The SSI-Cov method proved more robust, successfully identifying 11 circumferential modes up to 240 Hz. The proposed approach offers a significantly more efficient, cost-effective method for characterizing rolling tire dynamics, which is readily applicable to treaded tires and adaptable for on-road testing.
Laser Doppler Vibrometers (ORG) LDV (ORG) Operational Modal Analysis (ORG) TCA (ORG) FDD (ORG) Stochastic Subspace Identification (ORG)
Originally published by arXiv Physics Read original →