Noise Prediction of Traction Gear in High-Speed Electric Multiple Unit

Tang, Zhaoping; Chen, Z. X.; Sun, Jianjun; Hu, Yanping; Min, Zhao

doi:10.2507/ijsimm18(4)co20

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…The principle is an artificial system with intelligent information processing functions such as learning, association, memory, and pattern recognition. Its unique nonlinear adaptive information processing capability makes it an effective way to solve complex combinatorial optimization problems [ 16 ]. However, the generalization ability of neural network algorithms is limited due to the structure determination, over- and under-learning, and local convergence problems of neural network algorithms [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Bus Single-Trip Time Prediction Based on Ensemble Learning

Huang

Lei

Song

et al. 2022

Computational Intelligence and Neuroscience

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The prediction of bus single-trip time is essential for passenger travel decision-making and bus scheduling. Since many factors could influence bus operations, the accurate prediction of the bus single-trip time faces a great challenge. Moreover, bus single-trip time has obvious nonlinear and seasonal characteristics. Hence, in order to improve the accuracy of bus single-trip time prediction, five prediction algorithms including LSTM (Long Short-term Memory), LR (Linear Regression), KNN (K-Nearest Neighbor), XGBoost (Extreme Gradient Boosting), and GRU (Gate Recurrent Unit) are used and examined as the base models, and three ensemble models are further constructed by using various ensemble methods including Random Forest (bagging), AdaBoost (boosting), and Linear Regression (stacking). A data-driven bus single-trip time prediction framework is then proposed, which consists of three phases including traffic data analysis, feature extraction, and ensemble model prediction. Finally, the data features and the proposed ensembled models are analyzed using real-world datasets that are collected from the Beijing Transportation Operations Coordination Center (TOCC). Through comparing the predicting results, the following conclusions are drawn: (1) the accuracy of predicting by using the three ensemble models constructed is better than the corresponding prediction results by using the five sub-models; (2) the Random Forest ensemble model constructed based on the bagging method has the best prediction accuracy among the three ensemble models; and (3) in terms of the five sub-models, the prediction accuracy of LR is better than that of the other four models.

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Section: Introductionmentioning

confidence: 99%

Bus Single-Trip Time Prediction Based on Ensemble Learning

Huang

Lei

Song

et al. 2022

Computational Intelligence and Neuroscience

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“…Therefore, in order to evaluate the sound pressure level resulting from the meshing spur gear pair, different coupled Eulerian–Lagrangian analyses were carried out, putting the gears in an Eulerian mesh, which represented air (modeled as a perfect gas), since noise is characterized by pressure waves that propagate through a medium. A noise, vibration, and harshness (NVH) analysis could not be properly performed, since it could only be used to evaluate the noise resulting from rolling bearings and supporting system vibrations [ 48 , 49 , 50 , 51 ]. The following table ( Table 3 ) shows the parameters that characterized the different examined conditions.…”

Section: Resultsmentioning

confidence: 99%

Noise Reduction in Spur Gear Systems

Liguori

Armentani

Bertocco

et al. 2020

Entropy

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This article lists some tips for reducing gear case noise. With this aim, a static analysis was carried out in order to describe how stresses resulting from meshing gears affect the acoustic emissions. Different parameters were taken into account, such as the friction, material, and lubrication, in order to validate ideas from the literature and to make several comparisons. Furthermore, a coupled Eulerian–Lagrangian (CEL) analysis was performed, which was an innovative way of evaluating the sound pressure level of the aforementioned gears. Different parameters were considered again, such as the friction, lubrication, material, and rotational speed, in order to make different research comparisons. The analytical results agreed with those in the literature, both for the static analysis and CEL analysis—for example, it was shown that changing the material from steel to ductile iron improved the gear noise, while increasing the rotational speed or the friction increased the acoustic emissions. Regarding the CEL analysis, air was considered a perfect gas, but its viscosity or another state equation could have also been taken into account. Therefore, the above allowed us to state that research into these scientific fields will bring about reliable results.

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“…Copula theory was used to fit the conditional distribution of prediction error of multiple wind farms, so as to compensate the prediction error of current wind power load according to the prediction error of adjacent wind farms [15]. e simulation method is also the main method for making predictions [16,17].…”

Section: Related Workmentioning

confidence: 99%

Short-Term Electric Load Prediction and Early Warning in Industrial Parks Based on Neural Network

Wang¹,

Yang²,

Chen³

2021

Discrete Dynamics in Nature and Society

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This paper proposes a load forecasting method based on LSTM model, fully explores the regularity of historical load data of industrial park enterprises, inputs the data features into LSTM units for feature extraction, and applies the attention-based model for load forecasting. The experiments show that the accuracy of our prediction model and early warning model is better than that of the baseline and can reach the standard of application in practice; this model can also be used for early warning of local sudden large loads and identification of enterprise power demand. Therefore, the validity of the method proposed in this paper is verified using the historical dataset of industrial parks, and relevant technical products and business models are formed to provide value-added services to users by combining existing practical cases for the specific scenario of industrial parks.

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Noise Prediction of Traction Gear in High-Speed Electric Multiple Unit

Cited by 11 publications

References 16 publications

Bus Single-Trip Time Prediction Based on Ensemble Learning

Bus Single-Trip Time Prediction Based on Ensemble Learning

Noise Reduction in Spur Gear Systems

Short-Term Electric Load Prediction and Early Warning in Industrial Parks Based on Neural Network

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