Multimodel Fusion Health Assessment for Multistate Industrial Robot via Fuzzy Deep Residual Shrinkage Network and Versatile Cluster

To assess the health condition of industrial robots roundly and make hierarchical maintenance decisions, a multimodel fusion health assessment method is proposed for multistate industrial robots. Herein, many symptom parameters (SPs) are used to reflect the operation state of the industrial robot from aspects of vibration, temperature, and torque. Then, fuzzy deep residual shrinkage network is proposed to establish the SP-based status membership function as a single assessment model. The probabilities of robot operation states are determined and formulated as the hesitation fuzzy number (HFN). These HFNs from multiple assessment models are integrated into a collective hesitation fuzzy assessment matrix. Thus, the best worst method is adopted to estimate the confidence of each assessment model, and TOPSIS is used to judge the impact of different operation states on the industrial robot's behavior. Finally, a novel health index is defined for industrial robot, and robot health degree is identified by versatile cluster for hierarchical maintenance decisions. A self-built industrial robot test stand is adopted to validate the effectiveness of the proposed method, and sensitivity and comparison analysis results demonstrated that our method has advantages in terms of the situation adaptability and performance stability.