Benoit Iung

Prognostics and maintenance decision-making rely heavily on accurate and reliable measurements derived from sensors. However, sensor degradation introduces measurement uncertainties that compromise the precision of fault detection, remaining useful life estimation, and overall maintenance strategies. This paper provides a comprehensive review of the multifaceted impacts of sensor degradation on measurement uncertainty and its subsequent influence on prognostics and maintenance. The paper synthesizes various sensor degradation mechanisms and existing modelling techniques, emphasizing the growing research focus on developing accurate degradation models. The review also provides an in-depth analysis of how sensor degradation affects measurement uncertainty, exploring both qualitative and quantitative impacts through various modelling approaches and tools. Furthermore, this review examines the implications of this uncertainty on prognostics and maintenance decision-making methodologies, showcasing current mitigation methods and models. Finally, the review identifies key challenges and research gaps, outlining promising directions for future research in sensor degradation and its impact on prognostics and maintenance. By addressing these critical issues, this paper contributes to the advancement of more reliable, adaptive, and efficient Prognostics and Health Management (PHM) systems across various industrial and technological domains.

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.