Event-Based Data in Prognostics and Health Management: A Systematic Review of Models, Challenges, and Applications

Abstract

In the modern industrial context, Prognostics and Health Management (PHM) systems based on data-driven approaches have been widely and effectively developed to reduce maintenance costs. However, continuous data requires large memory capacity and high costs. Therefore, in recent years, the use of event-based data for PHM models has become prominent and increasingly attracts attention due to its cost-efficiency and effectiveness. This surge in data availability has opened new avenues for developing data-driven methods that leverage event patterns to enhance diagnostic, prognostic, and predictive maintenance capabilities. Building meaningful and interpretable patterns from raw event data is crucial for understanding system behavior, detecting faults early, forecasting future failures, and accurately estimating the Remaining Useful Life (RUL) of critical components. This review paper systematically surveys the state-of-the-art methodologies and frameworks for extracting, modeling, and utilizing event-based patterns in the context of diagnostic and prognostic applications.  Furthermore, we analyze challenges related to event data heterogeneity, scalability, and interpretability, as well as the need for robust pattern extraction methods that can adapt to dynamic operating environments. The review further explores how these event-based patterns contribute to building reliable diagnostic models, enabling early fault detection, and supporting maintenance decision-making through precise prognostics.Finally, this paper identifies key research gaps and outlines future directions, emphasizing the need for explainable, adaptive, and scalable pattern mining approaches that effectively translate raw event data into actionable maintenance intelligence. To address these challenges, we propose a conceptual framework that integrates advanced pattern discovery techniques with domain knowledge and feedback loops, enabling continuous learning and decision support. This comprehensive survey aims to serve as a foundational reference for researchers and practitioners committed to leveraging event data for enhanced system reliability and the development of optimized, intelligent maintenance strategies.