Data-model-driven prediction of comprehensive torsional vibration in the diesel engine crankshaft system

Abstract

To address the challenges of complex modeling and low evaluation efficiency in traditional dynamic models for assessing the torsional vibration characteristics of diesel engine crankshaft systems, this study constructs combustion-related and dimensionless characteristic parameters of the cylinder pressure signal, and selects cylinder pressure features highly correlated with the crankshaft system CTV (comprehensive torsional vibration, CTV). A crankshaft system CTV predictive approach of the crankshaft system were developed based on GA-BP and GSM-SVR. For the 50 samples covering different load conditions within the rotational speed range of 1000 r/min-2600 r/min, the GA-BP model achieves 93.79% accuracy, while GSM-SVR reaches 96.94%. A comparison of the different models reveals that GSM-SVR provides higher CTV prediction accuracy of the crankshaft system. This study provides guidance for the rapid prediction and suppression of torsional vibration in diesel engine crankshaft systems.