Uncertainty-guided evolvable recognition framework for industrial robots via prototype-based fuzzy inference and evidence fusion

Robotic systems and complex robotic mechanisms operating in industrial environments are increasingly required to perform long-term autonomous operation under evolving working conditions. In such scenarios, the continual emergence of previously unseen fault patterns pose significant challenges to recognition and predictive maintenance. Traditional machine learning models struggle in such class-incremental scenarios due to catastrophic forgetting and rigid architectures that cannot adapt to evolving fault patterns. More importantly, when confronted with ever-evolving open-set conditions where the label space remains entirely unknown and previously unforeseen samples continuously emerge in the absence of beforehand indication, these methods lose ability to recognize and discern unknown categories, thus fail to adapt to emerging new fault patterns. To address abovementioned limitations, this study presents an evolvable continual learning framework incorporating fuzzy rule inference and evidence fusion for industrial robotic fault diagnosis, combining a multi-scale feature extractor with Prototype-based Evolvable Fuzzy Inference System (PEFIS). The framework adaptively supplements and adjusts fuzzy rules to accommodate emerging fault types while mitigating catastrophic forgetting through multi-layer knowledge distillation and multi-evidence fusion for uncertainty quantification. The proposed method is applicable not only to scenarios with explicitly increasing categories but also to open-set environments where novel categories are continuously integrated without informing the system. Experimental results on real-world robotic datasets demonstrate its superior performance in both supervised and unsupervised class-incremental scenarios. The interpretable evolving mechanism and out-of distribution (OOD) recognition capability make the proposed method particularly suitable for real-world applications where noval fault patterns emerge unpredictably, advancing scalable and trustworthy AI solutions for industrial robotic systems requiring lifelong deployment.