Multi-source Variable Decoupling Network for Compound Fault Diagnosis of Train Bogie

When compound faults occur in rotating machinery, the mutual coupling and interference among different fault sources make it extremely difficult to directly isolate individual faults from the observed signals. Therefore, fault decoupling is essential prior to diagnosis. In this study, we propose a semi-supervised multi-source variable decoupling network (MVD-Net) that enables blind separation of unknown compound fault signals using only single-fault samples for training. First, low-dimensional features are extracted from the mixed signal through an encoder. These features are then mapped to multiple independent latent spaces corresponding to different fault sources via variational inference, while the number of sources is adaptively estimated using the evidence lower bound (ELBO). Subsequently, each source-specific decoder generates an estimated source signal from its corresponding latent representation. To ensure that each decoder focuses on a distinct fault component, a source-selective activation mechanism is incorporated into the decoding process, effectively mitigating the random assignment issue commonly encountered in traditional blind source separation methods. Finally, based on the estimated source signals, a separation mask is derived to extract individual sources from the original mixed signal. Two compound fault decoupling and diagnosis experiments were conducted on the BJTU-RAO dataset. The results demonstrate that compared with other methods, the proposed approach yields cleaner separated signals with more distinct time-frequency fault features and achieves higher diagnostic accuracy.