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