# Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models
###### Abstract  
This study presents a comparative analysis of three AutoEncoder (AE) models--Variational AutoEncoder (VAE), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)--to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization. Vertical acceleration data from a virtual wayside monitoring system serve as input for training the AE models, which are coupled with [PERSON]'s \(T^{2}\) Control Charts to differentiate normal and abnormal railway component behaviors. The results indicate that the SAE-\(T^{2}\) model outperforms its counterparts, achieving 16.67% higher accuracy than the CAE-\(T^{2}\) model in identifying distinct structural conditions, although with a 35.78% higher computational cost. Conversely, the VAE-\(T^{2}\) model is outperformed in 100% of the analyzed scenarios when compared to SAE-\(T^{2}\) in identifying distinct structural conditions while also exhibiting a 21.97% higher average computational cost. Across all scenarios, the SAE-\(T^{2}\) methodology consistently provided better classifications of wheel damage, showing its capability to extract relevant features from dynamic signals for Structural Health Monitoring (SHM) applications. These findings highlight SAE's potential as an interesting tool for predictive maintenance, offering improved efficiency and safety in railway operations. structural health monitoring; railways; damage detection; out of roundness; sparse autoencoder; convolutional autoencoder; variational autoencoder +
Footnote †: journal: Applied Sciences  
article  
[colorare major contributors to polygonal wear [4; 5], and manufacturing processes can also induce eccentricities [3; 6]. Furthermore, high-order polygonization at high speeds can significantly amplify axle box acceleration and wheel-rail normal forces and can even cause wheel-rail separation when resonance occurs [7]. Traditionally, verifications related to the assessment of the degradation process of railway structures are performed through visual inspections and manual measurements using devices such as tachometers and leveling instruments. These techniques present significant economic and technical challenges, particularly when applied on a large scale. Moreover, these methods often fail to detect internal and complex anomalies, notably in the case of initial damage [8], such as those associated with internal rail cracks and wheel polygonalization, compromising the safety and efficiency of railway operations [9]. Early detection of these problems is important, since as the damage worsens, it can lead to gradual deterioration of structural functionality, potentially exceeding acceptable safety limits and significantly increasing the likelihood of critical failures or collapses [10]. Automated railway monitoring techniques have significantly advanced in recent years, enhancing safety and efficiency in rail operations. Advances in sensor technology [11] and modal identification [12], which allow for feature extraction from vibration signals and data processing [13], have brought new possibilities for improving structural and railway monitoring, offering more precise and financially sustainable solutions [9; 14; 15]. A recent systematic review highlights the growing application of Wireless Sensor Networks (WSNs) in railway infrastructure monitoring, emphasizing their role in real-time data collection and analysis [16]. [PERSON] et al. (2023) [17] reviews three wheel-rail force measurement methods--Instrumented Wheelset (IWS), ground-based sensors, and indirect estimation--highlighting IWS as the most mature technique, with future advancements focusing on nondestructive, wireless, and continuous measurement for improved accuracy. Furthermore, the development of AI-based track-side monitoring systems has enabled the unsupervised detection and classification of train wheel defects, contributing to more effective maintenance strategies [2; 18; 19].