Accurate identification of compound mechanical faults through vibration analysis remains a critical challenge in industrial condition monitoring, primarily due to the nonlinear interaction of multiple failure modes and combinatorial explosion of potential fault combinations. While contemporary data-driven diagnostic methods demonstrate feature extraction capabilities when abundant labeled compound fault data exists, such idealized data conditions rarely occur in practical engineering applications. This paper presents a novel mechanism-guided decomposition diffusion network (McDDN) for resource-efficient compound fault diagnosis requiring only single-fault labeled samples for training. The proposed framework incorporates a physics-informed decomposition UNet (McD-UNet) within a diffusion-based learning architecture to disentangle overlapping fault signatures through mechanism-constrained signal separation. Feature mode decomposition (FMD) principles are mathematically encoded as regularization terms in the network's optimization objective, enabling data-driven learning of decomposition patterns while preserving physical interpretability. The diagnostic pipeline performs hierarchical fault identification through mechanism-guided signal decomposition into constituent single-fault components by single-fault classification. Experimental validation on the PU bearing dataset and BJTU-RAO high-speed train bogie dataset demonstrates superior performance, achieving 89.3% diagnostic accuracy for simultaneous bearing-gear-motor faults in multi-component rotating systems. Comparative analysis reveals 12-15% accuracy improvements over conventional model-based and pure data-driven benchmarks, validating the hybrid approach's effectiveness in knowledge-scarce compound fault scenarios.
Guo et al. (Wed,) studied this question.