The clutch judder has serious impacts on the NVH (noise, vibration and harshness) performance. In this paper, a simplified four-degree-of-freedom dynamic model with nonlinear friction torque and engine torque excitation is developed to simulate the clutch judder, and the stability and dynamic response of the clutch is analyzed based on the simplified model. In addition, the real part of judder modal frequency, the moment when the clutch enters the stick state, and the fluctuation level of the driving part of clutch are treated as the evaluation indexes of the judder performance. An uncertain hybrid model with random and interval variables is used to described the uncertainty of parameters and a hybrid perturbation vertex method is formulated to compute the uncertainty of the clutch judder. Furthermore, the parameters with high sensitivities are used as design variables and the uncertainty-based optimization are conducted to reduce the clutch judder.The optimization results have strongly validated that the proposed method is very effective to improve the robustness of the clutch judder performance.
The driveline torsional vibration issue is one of the most significant NVH (Noise, Vibration and Harshness) problems, especially in a rear wheel drive vehicle with a manual transmission. In this paper, a new driveline and rear axle coupled torsional vibration model (DRCTVM) is developed, which considers the relationship between the driveline and the rear axle. The experiments have shown that the DRCTVM can provide much better results compared with the traditional model. In addition, for the first time, the uncertainty theory is introduced to the analysis and optimization of driveline torsional vibration based on DRCTVM. A truncated normal distribution is used to describe the uncertainty of DRCTVM, which considers both the probability distribution and bounds of uncertain variables. Furthermore, the robustness of the driveline torsional vibration is analyzed by Monte Carlo process and optimized by Multi-Island Genetic Algorithm. The optimization results have strongly validated that the proposed model and method are very effective to improve the robustness of driveline torsional vibration performance.
Minimally invasive surgery, such as laparoscopic surgery, has developed rapidly due to its small wound, less bleeding and quick recovery. However, a lack of force feedback, which leads to tissue damage, is still unsolved. Many sensors have been used to offer force feedback but still limited by their large size, low security and high complexity. Based on the advantages of small size, high sensitivity and immunity to electromagnetic interferences, we propose a tactile sensor integrated with fiber Bragg gratings (FBGs) at the tip of laparoscopic grasper to offer real‐time force feedback in the laparoscopic surgery. The tactile sensor shows a force sensitivity of 0.076 nm/N with a repeatable accuracy of 0.118 N. A bench test is conducted in a laparoscopic training box to verify its feasibility. Test results illustrate that gripping force exerted on the laparoscopic grasper in terms of peak and standard deviation values reduce significantly for the novice subjects with force feedback compared to those without force feedback. The proposed sensor integrated at the tip of the laparoscopic grasper demonstrates a better control of the gripping force among the novice surgeons and indicates that the smart grasper can help surgeons achieve precise gripping force to reduce unnecessary tissue trauma.
The current single gas prediction model is not sufficient for identifying and processing all the characteristics of mine gas concentration time series data. This paper proposes an ARIMA-LSTM combined forecasting model based on the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) recurrent neural network. In the ARIMA-LSTM model, the ARIMA model is used to process the historical data of gas time series and obtain the corresponding linear prediction results and residual series. The LSTM model is used in further analysis of the residual series, predicting the nonlinear factors in the residual series. The prediction results of the combined model are compared separately with those of the two single models. Finally, RMSE, MAPE and R2 are used to evaluate the prediction accuracy of the three models. The results of the study show that the metrics of the combined ARIMA-LSTM model are R2 = 0.9825, MAPE = 0.0124 and RMSE = 0.083. The combined model has the highest prediction accuracy and the lowest error and is more suitable for the predictive analysis of gas data. By comparing the prediction results of a single model and the combined model on gas time series data, the applicability, validity and scientificity of the combined model proposed in this paper are verified, which is of great importance to accurate prediction and early warning of underground gas danger in coal mines.
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