# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 2 Related Works
### Predictive Maintenance Studies  
Predictive maintenance (PdM) research focuses on early fault detection through the use of historical data and prediction techniques such as machine learning algorithms, integrity analysis, mathematical models, and evaluation methodologies [16]. In [17], a study is presented that measures customer satisfaction at authorized service centers where vehicles receive preventive maintenance. However, instead of fault detection, the study focuses on estimating future maintenance dates for passenger vehicles, which involves periodic maintenance rather than predictive maintenance. While not directly addressing PdM, such approaches contribute to understanding maintenance planning and scheduling, a critical input for predictive models. Another study in [18] aims to predict wind turbine failures and estimate their remaining operational life. The data collected filters out errors in turbine operation, allowing critical information to be extracted for predictive analysis. A hybrid network combining Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) is used to train the prediction model, which demonstrates high accuracy, enhancing the efficiency of wind turbine maintenance. Although this work focuses on a non-automotive domain, its methodology--especially the hybrid CNN-SVM model--provides insights that can be transferred to vehicle maintenance prediction systems. [PERSON] et al. [19] propose a machine learning approach for the smart planning of fleet maintenance operations. This approach estimates the remaining time until the next required maintenance for each vehicle, employing Linear Regression, Random Forest, histogram-based Gradient Boosting, and SVM algorithms. This work exemplifies how supervised ML models can be adapted to different vehicle types and operational profiles, providing flexible solutions for fleet-wide maintenance optimization. In [20], the authors present a big data analytics framework for optimizing maintenance schedules through condition-based maintenance (CBM) and improving forecast accuracy for the remaining useful life (RUL) of assets. Similarly, ref. [21] introduces a PdMframework that predicts potential failures and quality defects, thereby improving overall production processes. Ref. [22] proposes a new PdM methodology using multiple classifiers to predict common integral-type faults in semiconductor manufacturing, failures caused by cumulative wear and tear. The authors combine different machine learning techniques to create a robust PdM approach. Ref. [23] presents a data-driven approach that classifies whether the remaining useful life of vehicles is shorter or longer than the time until the next scheduled service visit, integrating this with remaining useful life estimation. These studies collectively underline a clear trend: PdM research is moving beyond component-level diagnostics toward holistic asset lifecycle modeling. The integration of RUL estimation and service planning highlights the growing complexity and scope of modern PdM systems. Predictive maintenance (PdM) has become increasingly vital in the automotive sector, aiming to enhance vehicle reliability and reduce maintenance costs. Ref. [24] conducts a comprehensive literature review, highlighting the integration of artificial intelligence (AI) and machine learning (ML) techniques in PdM. Their study underscores the shift from traditional maintenance strategies to data-driven approaches, emphasizing the role of AI in forecasting maintenance needs and optimizing service schedules. Ref. [25] explored the application of digital twins (DTs) in supporting AI-guided predictive maintenance. Their review identifies the potential of DTs to provide real-time monitoring and simulation capabilities, facilitating proactive maintenance decisions. However, they also note challenges such as data integration and model accuracy that need to be addressed for effective implementation. Ref. [26] focuses on the importance of explainability in predictive maintenance models.