In recent decades, predictive maintenance has become a strategic solution to detect anomalies and anticipate failures in industrial equipment and sophisticated machines. This strategy relies on the continuous collection of multi-sensor data and performance indicators to feed machine learning algorithms capable of identifying early signs of malfunction, thereby enabling preventive interventions and reducing downtime. In the literature, three main approaches are described: physics-based, data-driven, and knowledge-based. Physics-based methods require accurate mathematical modeling of the physical degradation processes involved, but they are often difficult to apply to complex systems where physical laws are hard to formalize. Data-driven methods dominate current implementations due to their ability to learn complex patterns from large datasets. However, they suffer from several limitations, such as a lack of interpretability, dependence on large amounts of labeled data, and poor generalization to new operating conditions. Knowledge-based approaches, on the other hand, are more explainable and rely on expert-defined rules, but they struggle with adaptability and scalability in dynamic or uncertain environments. This study investigates the potential of generative artificial intelligence (Generative AI) to address these limitations by supporting the creation of hybrid predictive maintenance models that combine the strengths of both data-driven and knowledge-based approaches. Generative AI offers new capabilities to simulate realistic failure scenarios, augment limited datasets, and extract structured knowledge from unstructured technical sources. It can also support the construction of domain-specific knowledge graphs or ontologies by identifying relevant concepts and semantic relationships, as well as generate logical reasoning rules based on expert input or technical documentation. Additionally, Generative AI shows promising potential in assisting the physical modeling of complex systems by proposing plausible approximations or surrogate models when traditional analytical modeling proves difficult. To support this investigation, the study will address a use case focused on Remaining Useful Life estimation for aircraft engines using the C-MAPSS benchmark dataset.
Meriem Hafsi (Sun,) studied this question.