Abstract
Purpose: Tire maintenance is essential for safety, as tire failure can cause significant damage to human health and infrastructure during light rail operations. Therefore, we developed a diagnostic algorithm for light rail rubber tires to prevent such catastrophic events.BRMethods: Vibration signals measured by tri-axis accelerometers were acquired during real-world light rail operation, and we compared vibrations among various tire states, established a health index, and developed a model for determining tire state.BRResults: The most important type of vibration in terms of tire state was x-axis vibration. Our model determined tire states by calculating x-axis vibration differences in various tire conditions. Furthermore, high model accuracy was confirmed by stratified k-fold cross-validation.BRConclusion: Our diagnostic algorithm for determining tire condition reduces operating and support costs and promotes reliability and safety by enabling timely and appropriate maintenance that considers the age of individual tires. Thus, it could replace existing time-based maintenance protocols.
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