## 1 Introduction
This highlights the viability of interpretable and resource-efficient approaches in industrial contexts where large-scale labeled data is not readily available [8]. Additionally, a range of other contributions have applied Machine Learning to vehicle maintenance, further confirming its versatility and practical value across different operational settings [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. As seen, the automotive sector has produced several studies aimed at improving maintenance through failure prediction. In the context of urban bus transportation, where large fleets must be continuously monitored to ensure service availability, Machine Learning is playing an increasingly important role. By enabling the prediction of failures, maintenance interventions can shift from being reactive or fixed-schedule to a more flexible, condition-based approach. This not only helps maximize component usage but also reduces unexpected service interruptions [23; 24]. Several recent works have focused specifically on the application of Machine Learning (ML) in urban bus fleets. One study introduces an unsupervised method for fault detection in vehicles, including city buses, by analyzing onboard signal relationships without predefined models or labels [25]. Each vehicle identifies anomalous behavior locally and transmits compact models to an offline server, where deviations are detected through cross-vehicle comparison. The approach is designed for real-world constraints, limited bandwidth, no extra sensors, and high system variability and showed strong results in simulated and real bus data, comparable to supervised methods. Still, it is not suitable for all systems and faces challenges such as model complexity and data sparsity. Building upon that work, a semi-supervised approach called ICOSMO periodically updates the set of sensors used for anomaly detection based on fault repair records [26]. While it introduces a dynamic sensor selection mechanism within an IoT architecture for public bus fleets, it does not address supervised prediction tasks such as estimating fault duration, nor does it include model retraining or performance validation over time. Moreover, the ICOSMO model is still under development and has only been tested on a single vehicle. Complementing these anomaly detection strategies, another study explored Remaining Useful Life (RUL) prediction of turbo actuators in diesel engines as a way to improve preventive maintenance in bus fleets [27]. Among several models tested, the Accelerated Weibull Failure Time (AWFT) model outperformed deep learning approaches like TabNet and RNN, offering better accuracy, lower RMSE, and clearer feature interpretability. Using offline historical and snapshot data, the model enabled proactive maintenance planning despite limited post-warranty information, highlighting the value of wear-based predictions tailored to each fleet. More recently, unsupervised learning has gained traction in predictive maintenance for bus fleets, especially when labeled failure data is limited. One study tested a context-aware method on 19 urban buses, combining streaming anomaly detection with clustering and dimensionality reduction to capture inter-vehicle variability [28]. It achieved strong early fault detection (15-30 day horizon) and lower maintenance costs compared to baselines. Adapted TranAD and a hybrid detector proved effective in cost-sensitive scenarios, highlighting the importance of contextual modeling under data scarcity. In parallel, supervised models trained on historical service records have also been explored. A recent study applied dense neural networks, fed with both real and synthetically generated data, to capture real-world variability in failure patterns [29]. While the model achieved high specificity (0.896) and an AUC of 0.71, its low sensitivity (0.16) due to class imbalance revealed the need for better rebalancing strategies, reinforcing the importance of techniques such as SMOGN in practical applications.