Sensor-Based Operator Decision Support for Iterative Replication of Motorcycle Road-Vibration Signals on a Dual-Actuator Electrohydraulic Test Rig

The aim of this study was to develop and preliminarily validate an accelerometer-based method for objectively assessing the fidelity of laboratory-replicated motorcycle road vibrations and supporting the operator’s decision to terminate or continue the replication process. Reliable reproduction of real-world road inputs is necessary for repeatable investigations of vehicle dynamics and safety-relevant loads. Vertical front- and rear-axle accelerations recorded on different road surfaces were used as targets for a two-channel electrohydraulic test rig. The method combines percentage errors in response minima and maxima, retention of the best result obtained, time- and frequency-domain signal-fidelity assessment, and machine-learning prediction of the next trial. The retained responses exhibited high lag-compensated correlation and high in-band coherence. The extremum-based criterion detected local degradation that RMS error did not consistently reveal. A random-forest regressor produced a lower forecast error than a persistence baseline; however, the limited number of independent sequences does not support autonomous stopping. The proposed method provides a transparent, verifiable operator-support layer for sensor-based motorcycle testing. It may improve the repeatability and credibility of laboratory assessments of vehicle response to real-world road inputs, but validation over repeated sessions is required before automation.