针对柱塞式注水泵故障诊断面临的数据严重不平衡与特征提取困难问题，提出一种融合度量学习与采样适应机制的故障诊断算法。首先，设计基于拉普拉斯小波卷积和多尺度注意力机制的特征提取器，捕捉关键故障特征，并自适应分配特征权重。其次，引入一种基于双重中心损失的度量学习模块，在高维嵌入空间中施加样本类间距离约束和类内距离约束，促进同类故障样本的聚类，进一步提高故障识别能力。最后，采用类间平衡的批次采样机制对模型进行适应增强，克服数据不平衡对模型性能的制约，进一步提高模型的性能。利用真实的注水泵不平衡数据集验证所提方法，诊断准确率可达97.11%。

In response to the challenges of data imbalance and feature extraction encountered in fault diagnosis of plunger-type water injection pump, a fault diagnosis algorithm that integrates metric learning and sampling adaptation mechanism is proposed. Initially, a feature extractor based on Laplace wavelet convolution and multi-scale convolutional attention mechanism is designed to capture key fault characteristics at various scales and adaptively assign feature weights. Subsequently, a metric learning module based on dual center loss is introduced to enforce constraints in the high-dimensional embedding space, facilitating the clustering of same-class samples and increasing the classification capability. Finally, a class-balanced batch sampling mechanism is adapted to enhance the model, overcoming the limitations imposed by data imbalance and further improve the performance of the model. The proposed method is validated using an imbalanced dataset of water injection pump collected from industrial sites, achieving a diagnostic accuracy up to 97.11%.