This dataset provides monitoring data from a rotating electromechanical system under controlled and faulty conditions. The system is equipped with heterogeneous sensors, including accelerometers, current and voltage transducers, and temperature sensors trough both continuous signal and thermal images, to capture its physical behavior across a range of stationary and non-stationary rotation’s speeds. Differents faults, and some of the even with different severities, were systematically introduced in components of the electromechanical drivetrain—such as misalignments, bearing defects, and mechanical looseness—to simulate degradation scenarios typically encountered in industrial settings. The resulting multivariate time series data are suitable for a variety of applications, including machine learning-based diagnostics, signal processing, and condition monitoring. The availability of multiple sensor modalities enables advanced techniques such as information fusion and multi-sensor data analysis. All experiments were carried out under variable speed conditions, introducing dynamic complexities that enhance the dataset’s realism and usefulness for robust algorithm development. Environmental factors such as noise and voltage fluctuations further increase the dataset's value for tasks involving uncertainty quantification and signal preprocessing. This paper describes the experimental setup, sensor placement, fault injection procedures, and data acquisition parameters in detail.