Rotating machines are essential in numerous sectors, including railways, energy, and robotics. These machines exhibit unique degradation patterns and critical components that require monitoring. Despite the existence of various fault detection and diagnostic methods in the current literature, only few techniques that effectively consider heterogeneous data and variable operating conditions are published. Furthermore, a generalized approach for consistent monitoring across different systems remains challenging. Thus, our proposed work aims to enhance the generalization and application of these methods to diverse systems, emphasizing their robustness. For this purpose, we present multiple case studies featuring test benches of rotating machines with open-access data for monitoring purposes. Leveraging these datasets, we developed a processing methodology incorporating an efficient health indicator able to separate different health states for each system. The performance of our methodology is shown in each case study, by highlighting its flexibility across different monitoring data and operating conditions.