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Research and application of neural network for tread wear prediction and optimization

机译：神经网络胎面磨损预测和优化的研究与应用

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摘要

The wheel tread wear of heavy haul freight car in operation leads to shortened wheel turning period, reduced operation life, and poor train operation performance. In addition, wheel rail wear is a complex non-linear problem that integrates multiple disciplines. Thus, using a single physical or mathematical model to accurately describe and predict it is difficult. How to establish a model that could accurately predict wheel tread wear is an urgent problem and challenge that needs to be solved. In this paper, a tread wear prediction and optimization method based on chaotic quantum particle swarm optimization (CQPSO)-optimized derived extreme learning machine (DELM), namely CQPSO-DELM, is proposed to overcome this problem. First, an extreme learning machine model with derivative characteristics is proposed (DELM). Next, the chaos algorithm is introduced into the quantum particle swarm optimization algorithm to optimize the parameters of DELM. Then, through the CQPSO-DELM prediction model, the vehicle dynamics model simulates the maximum wheel tread wear under different test parameters to train and predict. Results show that the error performance index of the CQPSO-DELM prediction model is smaller than that of other algorithms. Thus, it could better reflect the influence of different parameters on the value of wheel tread wear. CQPSO is used to optimize the tread coordinates to obtain a wheel profile with low wear. The optimized wheel profile is fitted and reconstructed by the cubic non-uniform rational B-spline (NURBS) theory, and the optimized wear value of the tread is compared with the original wear value. The optimized wear value is less than the original wear value, thus verifying the effectiveness of the optimization model. The CQPSO-DELM model proposed in this paper could predict the wear value of different working conditions and tree shapes and solve the problem that different operating conditions and complex environment could have a considerable effect on the prediction of tread wear value. The optimization of wheel tread and the wear prediction of different tread shapes are realized from the angle of artificial intelligence for the first time.
机译：胎轮胎面磨损的重载货车在运行中导致车轮转向周期缩短，操作寿命减少，火车运行性能不佳。此外，车轮轨道磨损是一个复杂的非线性问题，整合多个学科。因此，使用单个物理或数学模型来准确描述和预测它是困难的。如何建立一个可以准确预测轮胎面磨损的模型是需要解决的迫切问题和挑战。在本文中，提出了一种基于混沌量子粒子群优化（CQPSO） - 优化导出的极限学习机（DELM）的胎面磨损预测和优化方法，即CQPSO-DELM，以克服这个问题。首先，提出了一种具有衍生特性的极端学习机模型（Delm）。接下来，将混沌算法引入量子粒子群优化算法中，以优化Delm的参数。然后，通过CQPSO-DELM预测模型，车辆动力学模型模拟不同的测试参数下的最大轮胎面磨损以训练和预测。结果表明，CQPSO-DELM预测模型的误差性能索引小于其他算法。因此，它可以更好地反映不同参数对车轮胎面磨损的价值的影响。 CQPSO用于优化胎面坐标以获得具有低磨损的轮廓。通过立方体非均匀RATIONAT B样条（NURBS）理论，优化的车轮轮廓拟合并重建，并且与原始磨损值进行比较胎面的优化磨损值。优化的磨损值小于原始磨损值，从而验证优化模型的有效性。本文提出的CQPSO-DELM模型可以预测不同工作条件和树形的磨损值，并解决不同的操作条件和复杂环境对胎面磨损值的预测具有相当大的影响。第一时间从人工智能角度实现了车轮胎面的优化和不同胎面形状的磨损。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2022年第1期|108070.1-108070.26|共26页
  • 作者

    Meiqi Wang; Sixian Jia; Enli Chen; Shaopu Yang; Pengfei Liu; Zhuang Qi;

  • 作者单位

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China School of Mechanical Engineering Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China;

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China School of Mechanical Engineering Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China;

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China School of Mechanical Engineering Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China;

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China School of Mechanical Engineering Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China;

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China School of Mechanical Engineering Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China;

    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China School of Mechanical Engineering Shijiazhuang Tiedao University 17 Northeast Second Inner Ring Shijiazhuang Hebei P.R.C. 050043 China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Extreme learning machine; Quantum particle swarm optimization; Tread optimization; Cubic NURBS theory;

    机译：极端学习机;量子粒子群优化;胎面优化;立方体NURBS理论;

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