Speed-based analysis and prediction of fatigue life for the crankshaft via genetic algorithm

Abstract

To improve the accuracy of crankshaft fatigue life prediction, a new method for predicting the fatigue life of crankshafts was proposed, considering the influence of rotational speed on fatigue life. This study investigates the relationship between engine speed and crankshaft fatigue life, and the engine speed will obviously affect the fatigue life of the crankshaft. Considering the influence of different engine speeds, a crankshaft fatigue life prediction model considering the engine speed is proposed. The method first obtains the crankshaft force time domain data under different loads through multi-body dynamics simulation, and analyzes the relationship between different engine speeds and crankpins force. The crankshaft fatigue life under different loads of the engine is calculated based on fatigue damage accumulation theory. Genetic algorithm is used to find the optimization of the relevant parameters of the crankshaft fatigue life prediction model considering the engine speed and determine the values of the parameters. The fatigue life and remaining fatigue life of the crankshaft is calculated using the proposed crankshaft fatigue life prediction model according to the engine speed signal. The proposed model in this study demonstrates a higher level of accuracy, as evidenced by the result showing a prediction error of 3.45%.