Abstract
Bearings and gears, as key rotating components, are prone to damage when operating under varying conditions with heavy loads, leading to equipment chain failures. In response to the demand for health monitoring based on vibration signals, this paper has constructed a vibration signal dataset for multiple fault types under varying operating conditions: the bearing dataset covers single faults such as inner ring, outer ring, rolling elements, and cage, as well as combined faults of inner and outer rings; the gearbox dataset includes typical defects such as tooth surface wear, broken teeth, and eccentricity; an additional mixed fault dataset of bearings and gearboxes is also provided. These datasets simulate real industrial scenarios through multiple dimensions, aiming to provide a reliable verification benchmark for intelligent diagnostic algorithms and improve the fault identification accuracy and model generalization ability under complex operating conditions.