An Intelligent Transmission Error Diagnosis Framework for Harmonic Reducers Based on Adaptive MPIM-FMD and Dual-Branch Fusion Swin Transformer

Intelligent fault diagnosis of harmonic reducers is essential for ensuring the reliability of precision industrial robot systems. Transmission error (TE) signals provide a direct and sensitive basis for early fault identification; however, multi-source coupling and weak fault features make complex fault representation challenging, thereby limiting the classification performance of diagnostic models. To address this issue, this paper proposes an intelligent diagnostic framework integrating adaptive MPIM-FMD decomposition and Fusion-SwinT multi-mode feature fusion. First, a multi-dimensional periodic impulse metric (MPIM) is constructed by considering impulsiveness, periodicity, and modal redundancy, and MOPSO is used to optimize the key FMD parameters for enhanced weak and coupled fault feature extraction. Then, the selected fault-related modes are encoded into MGAF and MSTFT image representations to capture global temporal correlations and local time–frequency evolution. Furthermore, DFEM and CMFM are introduced to enhance discriminative intra-modal features and promote cross-modal feature interaction. Finally, the fused features are classified using the Swin Transformer. Experiments on a self-developed harmonic reducer TE dataset demonstrate that the proposed method achieves superior performance in both single and compound fault identification and exhibits generalization capability under variable working conditions.