A human-machine cooperative health assessment method is proposed for mechanical equipment to evaluate its health condition and support hierarchical maintenance decisions. First, symptom parameters are extracted from collected vibration, pressure, and torque signals. A novel fuzzy residual shrinkage network is then developed to establish the status membership function of the mechanical equipment, forming the individual assessment model based on the extracted parameters. Next, the status memberships from each model are integrated into a collective hesitation fuzzy health assessment matrix. The Best-Worst Method is applied to calculate the priority of each assessment model, while TOPSIS with linguistic Z-numbers is employed to analyse the impact of different operational states on the equipment′s behaviour. Finally, a hesitation fuzzy weighted average operator is used to define the health index of the mechanical equipment, and health levels are identified using the versatile k-means clustering method to support hierarchical maintenance decisions. Validation results demonstrate that the proposed method excels in adaptability to different conditions and stability in performance.