Overhead cranes are critical installations in industrial environments, especially in steel plants where they are subjected to significant cyclic loads and stresses. Traditional inspection methods provide only periodic snapshots of a crane’s condition, leading to potential oversights and late-stage damage detection. This paper presents results obtained by a Structural Health Monitoring (SHM) system that leverages a digital twin approach to continuously evaluate the condition of overhead cranes. The SHM system combines data from strain sensors and operational data with a numerical model of the crane. The system calculates fatigue life consumption based on EN 13001 and identifies critical areas requiring inspections, thus preventing costly failures and extending the crane’s service life. In this study the SHM digital twin approach is illustrated using results obtained from a 12.5-ton single box girder crane. The loading histogram of the crane is accurately measured. Next to that, the strain data is used to calculate the dynamic load factor for hoisting, accelerating and braking of the crane and the trolley. Data is presented of a dataset with measured crane operation events. The obtained results are compared with values considered in crane design methods. With actual measured data, the fatigue damage can be more accurately calculated and remaining lifetime can be assessed with increased confidence.