With the trend toward higher boosting and lightweight design of diesel engines, the issue of vibration-induced failure of typical structural components has become increasingly prominent. Rapid and accurate prediction of overall engine vibration severity is key to evaluating and improving diesel engine reliability. To address the problems of low prediction accuracy of diesel engine vibration severity and insufficient vibration assessment capability for power density extension, this study comprehensively considers the combustion characteristics of cylinder pressure and dimensionless impact parameters. Multi-dimensional characteristic parameters highly sensitive to vibration severity (correlation coefficient > 0.8) are constructed and screened, namely average speed, cylinder pressure peak, maximum singular value of cylinder pressure, fourth-order cumulant of cylinder pressure, and kurtosis factor of cylinder pressure. Using the selected cylinder pressure and rotational speed characteristic parameters as inputs, a PO-SVR method for predicting diesel engine vibration severity was proposed. The prediction results for the test set samples are MAPE=3.04% and R2=0.98. The proposed vibration severity prediction method can provide guidance for rapid vibration assessment in the serial development of diesel engines.
Qidi Zhou, Tingting Sun, Yaozong Li et al.· Journal of Physics, Conferen...· 0 citations
Offshore wind turbine blade repair requires stable material removal and precise force regulation under curved‐surface contact and environmental disturbance. To address these challenges, this paper proposes an integrated constant‐force grinding method that combines a passive compliant end‐effector, a particle swarm optimization‐backpropagation neural network (PSO‐BP), and an improved active disturbance rejection control (ADRC) strategy. First, a passive compliant end‐effector with variable stiffness is designed to improve contact adaptability and reduce grinding impact on curved blade surfaces. Second, a PSO‐BP model is established to predict the material removal rate (MRR) and surface roughness (Ra) under different grinding conditions, thereby providing data‐driven support for process‐state evaluation and parameter scheduling. Third, based on a controller‐oriented force‐dynamics model, an improved ADRC framework integrating a tracking differentiator, nonlinear extended state observer, nonlinear state error feedback, and PSO‐BP‐assisted gain scheduling is developed for constant‐force grinding. A Lyapunov‐based analysis shows that the closed‐loop system is uniformly ultimately bounded under bounded disturbances and bounded scheduling error. Simulation and experimental results demonstrate that, compared with PID and standard ADRC, the proposed method achieves higher force‐tracking accuracy, stronger disturbance rejection, and better grinding quality. Under equivalent initial damage conditions, it produces the lowest post‐grinding surface roughness, indicating that the proposed method provides an effective solution for offshore blade grinding repair.
Yuhang Xue, Xinrong Liu, Tianhao Wang· Journal of Field Robotics· 0 citations
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