Within the paradigm of industry 4.0, predictive maintenance emerges as the pivotal strategic axis foe enhancing equipment reliability and optimization operating costs. This maintenance is fundamentally predicated on the utilization of artificial intelligence tools and data analysis techniques. In this context, the present article proposes a synthetic data generation approach dedicates to detecting failures of the ABB IRB 1100 industrial robot. To address the dearth of real-world data, a sophisti