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A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring

机译：一种贝叶斯机器学习方法，用于使用轨道侧监测的铁路轮缺陷的在线检测

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摘要

Wheel condition assessment is of great significance to ensure the operation safety of trains and metro systems. This study is intended to develop a Bayesian probabilistic method for online and quantitative assessment of railway wheel conditions using track-side strain-monitoring data. The proposed method is a fully data-driven, nonparametric approach without the need of a physical model. To enable defect identification using only response measurement, the measured dynamic strain responses of rail tracks during the passage of trains are processed to elicit the normalized cumulative distribution function values representative of the effect of individual wheels, which in conjunction with the frequency points are used to formulate a probabilistic reference model in terms of sparse Bayesian learning. Through cleverly realizing sparsity by introducing hyper-parameters and their priors, the sparse Bayesian learning makes the resulting model to exempt from overfitting and generalize well on unseen data. Only the monitoring data in healthy state are needed in formulating the reference model. A novel Bayesian null hypothesis significance testing in terms of scale-invariant intrinsic Bayes factor, which does not suffer from the Jeffreys–Lindley paradox, is then pursued in the presence of new monitoring data collected from possibly defective wheel(s) to detect wheel defects and quantitatively assess wheel condition. The proposed method in fully Bayesian inference framework is verified by utilizing the real-world monitoring data acquired by a distributed fiber Bragg grating–based track-side monitoring system and comparing with the offline inspection results.
机译：轮状况评估具有重要意义，以确保火车和地铁系统的运行安全性。本研究旨在使用轨道侧应变监测数据开发用于在线和定量评估铁路轮状况的贝叶斯概率方法。该方法是完全数据驱动的非参数方法，而无需物理模型。为了使用仅使用响应测量实现缺陷识别，处理在列车通过期间的轨道轨道的测量动态应变响应，以引出代表各个轮子的效果的归一化累积分布函数值，其与频率相结合在稀疏贝叶斯学习方面制定概率参考模型。通过引入超参数及其前沿来巧妙地实现稀疏性，稀疏的贝叶斯学习使得产生的模型免于过度接受和概括在看不见的数据上。仅在制定参考模型时只需要在健康状态下进行监测数据。在从可能有缺陷的轮子中收集的新监测数据存在以检测车轮缺陷的情况下，新的贝叶斯内在贝叶斯因子方面的新型贝叶斯内在凸起的显着性测试和定量评估轮状况。通过利用由基于分布式光纤布拉格光栅的轨道侧监测系统获取的实际监测数据并与离线检查结果进行比较，验证了完全贝叶斯推理框架中的提出方法。

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