面向油田注水泵复合故障诊断面临的辨识困难和解耦问题，提出一种基于自适应时频模态分解与多路径融合Transformer的故障诊断算法。首先，设计一种基于时频变换预处理和自适应机制的变分模态分解算法，捕捉复合故障子频带的关键故障特征，并自适应调整频带宽和频谱中心参数。其次，设计一种具备多路径融合能力的Transformer深度学习模块，在高维嵌入空间中施加局部路径和全局路径注意力机制，增强模型对频率分布差异的建模能力，提高其识别复合故障特征的能力。最后，在注水泵数据集上验证模型，在复合故障零样本的训练条件下，该模型的诊断准确率相较于现有主流复合故障诊断模型平均提升了11.12%。

To address the challenges of fault identification and decoupling in composite fault diagnosis of oilfield plunger-type water injection pumps, this study proposes a fault diagnosis algorithm based on adaptive time-frequency variational mode decomposition（VMD） and a multi-path fusion Transformer. First, an adaptive VMD algorithm incorporating time-frequency transformation preprocessing and adaptive mechanisms is designed to capture key fault features within composite fault sub-bands while dynamically adjusting the bandwidth and spectral center parameters. Then, a Transformer-based deep learning module with multi-path fusion capability is developed, which introduces both local path and global path attention mechanisms in a high-dimensional embedding space to enhance the ability to model frequency distribution discrepancies and improve composite fault feature recognition. Finally, experiments on a water injection pump dataset validate the proposed model. Under zero-sample training conditions for composite faults, the model achieves an average diagnostic accuracy improvement of 11.12% compared with mainstream composite fault diagnosis methods.