Agricultural vehicles operating in rough environments experience increased
fatigue damage accumulation, which may decrease machine safety and reliability.
Autonomous agricultural machines offer an opportunity to incorporate fatigue
damage considerations into path planning.
This work investigates whether machine learning can predict fatigue damage to a
tractor chassis using light detection and ranging (LiDAR)-based terrain
features, vehicle speed, and rotational vehicle state data (e.g., triaxial
angle, angular velocity, and angular acceleration). Fatigue damage was estimated
using the Rupp filter and the Durability Transfer Concept. Following poor
predictive performance of the machine learning models, an exploratory analysis
of damage histograms, dominant frequency, and acceleration magnitude was
performed.
Results indicated that most estimated fatigue damage occurred in the 0–2 Hz band,
which coincides with the frequency range of terrain-induced acceleration.
On-road driving led to the greatest fatigue damage, potentially due to the
harder driving surface and increased vehicle speed. Differences between root
mean square (RMS) acceleration magnitude and fatigue damage indicate that
isolated high-magnitude events may have contributed to increased estimated
fatigue damage.
Several suggestions for future development were identified. Identification of the
endurance limit of the tractor chassis will permit the removal of nondamaging
events, improving label accuracy. Furthermore, the presence of a front-loader
implement may have impacted chassis acceleration. Thus, a comprehensive dataset
with multiple implement configurations is needed to determine the influence of
implement configuration on dynamics and resultant damage.
