Industry 4.0 will benefit significantly from the ongoing advancements in artificial intelligence, particularly with regard to predictive maintenance. By continuously monitoring and analysing real-time data of a system, proactive maintenance actions can be taken before any major issues arise. Incorporating prognostics and health management allows for assessing the health of a system and predicting its future state based on current operating conditions. However, a major challenge in health modelling within manufacturing environments is modelling systems that are experiencing trend-based degradation. The field of theory-guided data science offers potential solutions by integrating prior knowledge about the system directly into data-driven methods, providing a hybrid approach to effectively implement prognostics and diagnostics in the context of Industry 4.0.