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A Novel Machine Learning Technique for Online Health Monitoring of High-speed Trains



To ensure the operation safety and ride comfort of high-speed trains, a combination of smart sensory systems and intelligent identification models for online condition monitoring and assessment is highly desired. During routing operations, various dynamic responses induced by the wheel-rail interaction can cause severe wheel defects. The deterioration of train wheels, normally classified as “out-of-roundness” (OOR), can seriously threaten the operation safety and cause catastrophic derailment events. Conventional model-based prognostic methods often require an in-depth understanding of the wheel-track system to develop favorable mathematical models that are rather cumbersome. To complement the deficiencies of model-based prognostic approaches, the use of data-driven methods has been increasingly applied to various engineering fields. This research introduces a random forest (RF)-based method for online condition prediction and monitoring of train wheels. The RF-based method is a novel machine learning technique that possesses good stability and high accuracy for data classification with less parameter adjustment in modeling processes. A crucial step for the successful implementation of the RF-based technique is the data mining process to extract valuable feature information from the raw data. Therefore, the Teager-Kaiser energy operator (TKEO) and the wavelet packed decomposition (WPD) technique are integrated together for feature extraction in this work. The optimized feature subsets can thus be employed in the presented data-driven model for the online health monitoring of high-speed train wheels.

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