## 1 Introduction
Finally, some contributions have proposed IoT-integrated ML frameworks aimed at real-time predictive maintenance in hybrid and electric bus fleets [30]. By leveraging onboard sensor data, such as engine temperature, brake wear, and battery voltage, these systems dynamically adjust maintenance schedules to reduce failures and improve fleet reliability. While the results were promising, key challenges were also noted, including data security, system interoperability, and the need for algorithmic refinement to ensure scalable deployment. Additionally, other recent contributions [31; 32] further reinforce the potential of Machine Learning for predictive maintenance in urban bus fleets, addressing diverse components and use cases that complement the approaches discussed above. Despite the growing interest in data-driven maintenance strategies within public transport systems, there is still a gap in studies providing clear, reproducible, and open-source pipelines that maintenance teams can adopt under real operational conditions. Most existing works assume full digitalization and access to large, well-curated datasets, an assumption that rarely matches the early stages of digital transformation, where data is limited, heterogeneous, and constrained by technological or organizational barriers. In these contexts, teams often lack not only robust analytical tools but also practical methodological guidance to extract value from the operational data already being recorded by modern vehicles. To bridge the identified methodological gap, this study proposes and validates a reproducible Machine Learning pipeline for predictive maintenance in urban bus fleets operating under data scarcity conditions. The pipeline builds upon a previous study [33] but moves one step further by targeting online-operational applicability in constrained environments. The prior study was conducted using a rich historical dataset and did not aim to develop or deploy a predictive model for real-time support. Instead, it focused on analyzing past regeneration behavior to extract insights into the control logic of the DPF system, using explainable artificial intelligence to assist maintenance teams in understanding and improving existing practices. Moreover, that earlier work focused exclusively on the active regeneration phase of the DPF. In contrast, the present study targets the preceding clogging phase, characterized by progressive soot accumulation. The main contributions of this work can be summarized as follows:  
* Development of a reproducible Machine Learning pipeline tailored for data-scarce environments in urban bus fleets, enabling the deployment of predictive maintenance strategies without requiring extensive historical datasets or full digitalization. * Systematic evaluation of lightweight and interpretable algorithms (Linear Regression, Ridge Regression, Decision Trees, KNN), in conjunction with techniques suitable for small datasets (SMOGN augmentation and Leave-One-Out Cross-Validation). * Implementation of a scheduled batch retraining strategy to enable long-term model adaptability in the face of operational changes and data drift, validated through a real-world case study on Diesel Particulate Filter (DPF) clogging. * Proposal of a scalable and transferable framework that can be adapted to other fleets and operational scenarios, supporting digital transformation efforts in public transport systems. By combining these innovations, the proposed approach enables fleet operators to make proactive decisions, reduce service interruptions, and progressively scale their data-driven capabilities. To demonstrate the applicability of the proposed pipeline under real operational conditions, a real-world use case was examined: The progressive clogging of the Diesel Particulate Filter (DPF) in hybrid diesel buses operating in an urban transport fleet.