Machine learning classification algorithms play a major role in diagnosing faults in industrial equipment. In this paper, we investigate the use of ensembles composed of deep neural networks for improving the electrical submersible pump fault diagnosis results. The proposed method relies on composing an ensemble of multiple convolutional neural networks trained with a metric function for extracting relevant features directly from the raw data. The final classification is given by a standard voting scheme after a Random Forest is trained for each feature set generated by each deep metric neural network and then the methods are compared with two previous methods already used for the electrical submersible pump fault diagnosis. The experiments were carried out using five different metric functions: Proxy-Anchor loss, CosFace loss, Triplet loss, Lifted Structured loss and Contrastive loss. Results show statistical evidence that the new approach using ensemble methods achieves better performance than the previous solutions. Moreover, results indicate that composing an ensemble of multiple distinct metric losses achieves a high macro F-measure with low variance when compared to an ensemble where all neural networks are trained with the same metric loss.

机器学习分类算法在诊断工业设备故障方面发挥着重要作用。在本文中，我们研究了使用由深度神经网络组成的集成来改进电潜泵故障诊断结果。所提出的方法依赖于组合使用度量函数训练的多个卷积神经网络的集合，以直接从原始数据中提取相关特征。在为每个深度度量神经网络生成的每个特征集训练随机森林后，通过标准投票方案给出最终分类，然后将这些方法与已经用于电潜泵故障诊断的两种先前方法进行比较。实验使用五种不同的度量函数进行：Proxy-Anchor loss、CosFace loss、Triplet loss、提升结构损失和对比损失。结果显示统计证据表明，使用集成方法的新方法比以前的解决方案实现了更好的性能。此外，结果表明，与所有神经网络都以相同的度量损失进行训练的集合相比，组合多个不同度量损失的集合可以实现高宏观 F 度量和低方差。