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

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.