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This study proposes a learning-based method with domain adaptability forinput estimation of vehicle suspension systems. In a crowdsensing setting forbridge health monitoring, vehicles carry sensors to collect samples of thebridge's dynamic response. The primary challenge is in preprocessing; signalsare highly contaminated from road profile roughness and vehicle suspensiondynamics. Additionally, signals are collected from a diverse set of vehiclesvitiating model-based approaches. In our data-driven approach, two autoencodersfor the cabin signal and the tire-level signal are constrained to force theseparation of the tire-level input from the suspension system in the latentstate representation. From the extracted features, we estimate the tire-levelsignal and determine the vehicle class with high accuracy (98% classificationaccuracy). Compared to existing solutions for the vehicle suspensiondeconvolution problem, we show that the proposed methodology is robust tovehicle dynamic variations and suspension system nonlinearity.

Transfer Learning for Input Estimation of Vehicle Systems