## 1 Introduction  
Asset management across industries has undergone a substantial transformation in recent decades. With the introduction and exponential growth of technologies such as Machine Learning (ML), the Internet of Things (IoT), cyber-physical systems, and advanced sensorization, companies across various sectors are now capable of recording data from their assets, transmitting it in real time, and accessing it remotely from anywhere in the world. Moreover, the use of Artificial Intelligence (AI) enables the prediction of operating conditions based on the complex interactions among system components. This approach marks a significant shift in the way maintenance has been conducted in recent decades, evolving from traditional preventive strategies, based on fixed schedules, and corrective actions triggered by failure conditions, to a predictive, data-driven methodology [1, 2]. Nevertheless, this new maintenance paradigm introduces a series of obstacles that must be addressed to ensure effective implementation. One of the primary barriers is the limited technical expertise within the current workforce, which can hinder the adoption of advanced digital tools. In addition, the required initial investment constitutes a considerable constraint, particularly due to the infrastructure necessary to fully exploit asset data. This includes both the deployment of platforms capable of processing and visualizing data in real time and the potential hardware adaptations needed to enable equipment to collect, transmit, and respond to information efficiently [1; 2; 3; 4]. Recent research has increasingly explored how Machine Learning (ML) supports the digital transformation of maintenance, particularly by enhancing diagnostics, anticipating failures, and optimizing intervention timing in the automotive sector. These studies adopt diverse strategies and tools tailored to specific operational scenarios. For example, one approach proposes a remote diagnosis and maintenance system based on Least Squares Support Vector Machines (LS-SVM), capable of estimating the Remaining Useful Life (RUL) of vehicle components using sensor data and Diagnostic Trouble Codes (DTCs) [5]. The method applies multiple classifiers per subsystem to improve accuracy, and, in a gearbox case study, LS-SVM outperformed KNN. However, low correlation among sensor inputs emerged as a key limitation, addressed through modular classification. Another contribution presents an ensemble anomaly detection system that combines one-class and two-class classifiers to identify both known and unknown faults in automotive systems using multivariate time-series data from OBD-II during road trials [6]. The system achieved strong performance (F2-score = 83.3%) in detecting unseen faults under operand driving conditions. While developed for offline analysis, its structure supports potential deployment in online predictive maintenance contexts. A different approach focuses on predicting air compressor failures in commercial trucks using Random Forest models trained on historical workshop data [7]. Feature selection techniques, such as beam search, enhanced performance over expert-defined variables. Limitations included inconsistent labeling, sparse sensor readings, and data quality issues. The model was implemented in an offline setting, processing data downloaded during scheduled maintenance intervals, offering a scalable and realistic strategy for operational fleets. In data-scarce predictive maintenance scenarios, classical Machine Learning models, when combined with structured feature engineering pipelines, can outperform deep learning approaches both in performance and efficiency. A recent study demonstrated that, even with a limited and imbalanced dataset, traditional classifiers such as MLP and Random Forest, supported by a modular preprocessing pipeline, achieved superior detection of critical fault states compared to CNN and LSTM architectures.