电子测量与仪器学报

何静,闫思峰,张昌凡.基于改进复杂度追踪的轮轨力鲁棒检测方法[J].电子测量与仪器学报,2025,39(12):129-137

基于改进复杂度追踪的轮轨力鲁棒检测方法

Robust wheel-rail force detection method based on improved complexity pursuit

DOI：

英文关键词:complexity pursuit blind source separation gradient descent wheel-rail force

基金项目:国家自然科学基金(52572375，52172403，62303178) 、湖南省自然科学基金(2025JJ70058)、湖南省教育厅科学研究项目(25A0798)项目资助

作者	单位
何静	湖南工业大学交通与电气工程学院株洲412007
闫思峰	湖南工业大学交通与电气工程学院株洲412007
张昌凡	湖南工业大学交通与电气工程学院株洲412007

Author	Institution
He Jing	College of Transportation and Electrical Engineering, Hunan University of Technology, Zhuzhou 412007, China
Yan Sifeng	College of Transportation and Electrical Engineering, Hunan University of Technology, Zhuzhou 412007, China
Zhang Changfan	College of Transportation and Electrical Engineering, Hunan University of Technology, Zhuzhou 412007, China

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中文摘要:

轮轨力是衡量铁路安全性的核心指标，获取轮轨力数据最直接且有效的方式是利用巡检车上的测力轮对，但是，从测力轮对采集到的数据往往包含多种干扰因素，这使得准确评估铁路状况变得复杂，而现有的算法难以从现实的复杂干扰中分离出所需的轮轨力信号。为此，本文提出了一种融合改进型复杂度追踪算法与信号特征提取一体化的轮轨力鲁棒检测方法。首先，采用新型小批量迭代策略从轮轨力总数据集抽取子集作为小批量样本，提升了算法的全局寻优能力，避免了陷入局部极值；其次，使用基于自适应学习率调度器的梯度下降算法进行复杂度追踪，有效地优化了模型的收敛速度和整体性能，更适用于实际工程；然后，利用Hilbert-Huang变换方法对分离得到的轮轨力源信号提取特征参数。最后，经过实际轮轨力数据实验验证，结果表明该检测方法能够有效地从混杂信号中分离出轮轨力信号，并准确地提取特征参数，为铁路安全状态监测提供了有力的数据支撑。

英文摘要:

Wheel-rail force is a core indicator for measuring railway safety. The most direct and effective way to obtain wheel-rail force data is to use the force-measuring wheel pairs on inspection vehicles. However, the data collected from the force-measuring wheel pairs often contains multiple interference factors, which makes the accurate assessment of railway conditions complex. Moreover, existing algorithms are difficult to separate the required wheel-rail force signals from the complex interferences in reality. To this end, this paper proposes a robust wheel-rail force detection method that integrates an improved complexity tracking algorithm with signal feature extraction. Firstly, a new small-batch iterative strategy is adopted to extract subsets from the total wheel-rail force data set as small-batch samples, which enhances the global optimization ability of the algorithm and avoids getting trapped in local extremum. Secondly, the gradient descent algorithm based on the adaptive learning rate scheduler is used for complexity tracking, which effectively optimizes the convergence speed and overall performance of the model, making it more suitable for practical engineering. Then, the Hilbert-Huang transform method is utilized to extract the characteristic parameters of the separated wheel-rail force source signals. Finally, through the experimental verification of actual wheel-rail force data, the results show that this detection method can effectively separate the wheel-rail force signals from the mixed signals and accurately extract the characteristic parameters, providing strong data support for the monitoring of railway safety conditions.

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