The rapid digital transformation of industrial systems in the 21st century has led to an exponential growth in data generated by manufacturing processes and end-user products, particularly in the automotive sector. While this big data creates new opportunities for monitoring and diagnostics, it also introduces significant challenges related to system complexity, scalability, and nonlinearity, as well as the increasing shortage of experienced domain experts. These challenges motivate the adoption of intelligent, automated fault and anomaly detection techniques capable of operating reliably under real-world conditions. The primary objective of this paper is to provide a comprehensive and structured review of the anomaly detection methodologies for automotive applications, with particular emphasis on intelligent fault diagnosis, tolerance, and monitoring architectures. To this end, the paper systematically categorizes existing approaches, including model-based, data-driven, and hybrid techniques, and analyzes their underlying principles, data requirements, computational complexity, and applicability to safety-critical systems. Based on this analysis, the paper highlights current limitations, open research challenges, and emerging trends, including the integration of machine learning and artificial intelligence with domain knowledge and control-oriented frameworks. The main contribution of this work is a unified perspective that supports researchers and practitioners in selecting, designing, and deploying effective anomaly detection solutions for next-generation automotive and cyber-physical systems.
Derse et al. (2026) studied this question.