SMNet: A Novel Compositional Generalization Model for Industrial Robot Multijoint Fault Diagnosis

Abstract

Compound fault diagnosis in multi-joint industrial robots is a critical yet underexplored problem in industrial internet of things, where the simultaneous degradation of multiple joints poses a severe challenge for reliable operation. Unlike conventional methods limited to single-fault scenarios, this paper addresses the compositional generalization challenge—requiring models trained only on simple faults to accurately recognize unseen higher-order fault compositions. To this end, we propose StateMix Network (SMNet), a multi-stage architecture that preserves atomic joint-level representations before compositional diagnosis. Specifically, a Single-Joint Feature Extraction (SJFE) backbone extracts clean joint-private features, which are then fused by an Attention-Guided Dilated Fusion (AGDF) neck employing parallel Cascaded Dilated Convolution Blocks (CDCBs) bracketed by a dual-path attention mechanism for scale- and context-aware integration. Finally, a Mamba-based sequence mixer models long-range cross-joint dependencies to capture global fault dynamics. Extensive experiments on in-situ vibration data from a single six-joint industrial robot platform, under a strict train-on-simple/evaluate-on-complex protocol, demonstrate that SMNet consistently outperforms representative baselines in macro-Precision, Recall, and F1-score, particularly on unseen triple- and quadruple-joint compositions. Ablation and sensitivity analyses further validate the effectiveness of each module. This work presents a diagnostic approach that effectively generalizes from simple to complex fault scenarios in industrial robots.