A Multibody Dynamic Modeling and GAN–CNN Fusion Framework for Small-Sample Fault Diagnosis of Open-Pit Coal Mine Reducers

To address fault diagnosis under limited sample conditions, this paper proposes a small-sample diagnosis framework integrating multibody dynamic modeling and a GAN–CNN fusion strategy. First, a rigid–flexible coupled multibody dynamic model of the reducer is established to simulate vibration responses under typical fault modes, including broken gear tooth, gear wear, and bearing outer ring fault, thereby generating representative simulation samples. Second, to reduce the distribution discrepancy between simulated and measured data, the simulated samples are introduced into a generative adversarial learning framework for feature enhancement, with limited measured samples used as references. Cosine similarity is employed to evaluate the consistency between the enhanced simulated data and the measured data in the feature space. Finally, the enhanced simulated samples are fused with measured samples to construct a hybrid dataset for convolutional neural network training and fault classification. Experimental results show that the proposed framework improves the similarity between simulated and measured data, with cosine similarity increasing from below 0.65 to above 0.80. Under small-sample conditions, the mean diagnosis accuracy reaches 83.81%, which is 17.33 percentage points higher than that obtained using the original small-sample dataset. The proposed framework provides an effective modeling and algorithmic approach for reducer fault diagnosis under data-scarce conditions.