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

Yi‐Qing Ni, Qiu-Hu Zhang

Structural Health Monitoring 2020 article cited by 64

importance 6/10 ai assisted Structural Health Monitoring Techniques rail transport stage: research

Detecting railway wheel defects online and quantitatively using only track-side strain-monitoring data, without a physical model.

Wheel condition assessment is significant for the operation safety of trains and metro systems.

A sparse Bayesian learning method built a probabilistic reference model from processed dynamic strain responses to identify defective wheels in a fully data-driven, nonparametric way.

not stated in source (abstract truncated before validation results)

Proposes a data-driven online method for railway wheel condition assessment.

Venue Structural Health Monitoring
Published 2020
Work type article
Authors Yi‐Qing Ni, Qiu-Hu Zhang
Citations 64 (OpenAlex, at import time)
References 44
Model or method Sparse Bayesian learning (Bayesian machine learning)
Organization not stated in source
Primary source https://doi.org/10.1177/1475921720921772
Open access hybrid
Keywords railway wheels, defect detection, Bayesian machine learning, track-side monitoring
6 / 10

significant — thousands to millions affected

Abstract states wheel condition assessment is of great significance for operation safety of trains and metro systems; no scale quantified.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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