基于AR模型盒维数的概率筛综合动态性能预测

IF 0.6 Q4 ENGINEERING, MECHANICAL Journal of Measurements in Engineering Pub Date : 2023-11-13 DOI:10.21595/jme.2023.23522
Qingtang Chen, Yijian Huang
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引用次数: 0

摘要

为了评价概率筛的综合动态性能,选择合适的工况,建立了概率筛振动系统的动态模型。然后,采用基于振动试验时间序列自回归(AR)模型的动力特性参数计算方法。分析了概率筛振系统的综合动态特性、筛分效率与箱维数之间的关系,利用最小二乘支持向量机(LSSVM)、广义回归神经网络(GRNN)和反向传播神经网络(BPNN)对筛分效率与箱维数之间的关系进行预测。分析结果表明,系统的筛分效率、稳定性、响应速度和综合动态特性均与时间序列的盒维数有关。概率筛振动系统的复杂性影响着概率筛的综合动态性能,最终影响到概率筛的筛分效率;根据箱体尺寸与工况参数之间的曲线选择系统的最佳工况;以箱体维数作为唯一输入变量,采用LSSVM、GRNN和BPNN方法对筛分效率的预测精度高,预测结果稳定可靠,且箱体维数可作为单一输入变量预测筛分效率,具有输入参数少、预测效率高、预测精度高等优点,具有拓展应用空间和进一步研究价值的巨大潜力。
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Prediction of comprehensive dynamic performance for probability screen based on AR model-box dimension
In order to evaluate the comprehensive dynamic performance of probability screen and select the appropriate working conditions, a dynamic model of probability screen vibration system is established. Then, the calculation method of the dynamic characteristic parameters, based on the time series Auto Regression (AR) model of vibration test, is used. The relationship among the comprehensive dynamic characteristics, the screening efficiency and the box dimension of probability screen vibration system is analyzed, and Least Square Support Vector Machine (LSSVM), Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to predict the screening efficiency with box dimension. The analysis result shows that the screening efficiency, the stability, the response rapidity and the comprehensive dynamic characteristic of the system are all related to the box dimension of time series. As for the complexity of probability screen vibration system, it affects the comprehensive dynamic performance, and ultimately touches the screening efficiency of the probability screen; The best working conditions for the system are selected by the curve between box dimension and the working condition parameter; Taking box dimension as the only input variable, the prediction accuracy of the screening efficiency is high by using LSSVM,GRNN and BPNN methods, the prediction results are stable and reliable, and the box dimension can be used as a single input variable to predict the screening efficiency, it has the advantages of fewer input parameters, high prediction efficiency, and high prediction accuracy, which has great potential for expanding application space and further research value.
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来源期刊
Journal of Measurements in Engineering
Journal of Measurements in Engineering ENGINEERING, MECHANICAL-
CiteScore
2.00
自引率
6.20%
发文量
16
审稿时长
16 weeks
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