This chapter presents an approach for generating research data to investigate the integration of data-driven and knowledge-based approaches for fault detection and failure prediction. The proposed method employs a simplified physical factory simulation model, augmented with additional sensors, software, and hardware to replicate the characteristics of an Industry 4.0 manufacturing system. The system facilitates efficient, low-cost data generation while capturing a diverse range of fault and failure scenarios, including physical degradation, sensor faults, and control issues, through a failure simulation engine. The generated data was validated by comparing simulated fault progression with expected real-world failure patterns, demonstrating the system’s effectiveness.