Nowadays, the industrial Internet of Things (IIoT) has been successfully utilized in smart manufacturing. The massive amount of data in IIoT promote the development of deep learning-based health monitoring for industrial equipment. Since monitoring data for mechanical fault diagnosis collected on different working conditions or equipment have domain mismatch, models trained with training data may not work in practical applications. Therefore, it is essential to study fault diagnosis methods with domain adaptation ability. In this article, we propose an intelligent fault diagnosis method based on an improved domain adaptation method. Specifically, two feature extractors concerning feature space distance and domain mismatch are trained using maximum mean discrepancy and domain adversarial training respectively to enhance feature representation. Since separate classifiers are trained for feature extractors, ensemble learning is further utilized to obtain final results. Experimental results indicate that the proposed method is effective and applicable in diagnosing faults with domain mismatch.

中文翻译：

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通过融合域对抗训练和集成学习的最大平均差异进行智能故障诊断


如今，工业物联网（IIoT）已成功应用于智能制造。工业物联网中的海量数据促进了基于深度学习的工业设备健康监测的发展。由于在不同工况或设备上收集的机械故障诊断监测数据存在域不匹配，使用训练数据训练的模型可能无法在实际应用中发挥作用。因此，研究具有领域适应能力的故障诊断方法十分必要。在本文中，我们提出了一种基于改进的域自适应方法的智能故障诊断方法。具体来说，分别使用最大均值差异和域对抗训练来训练关于特征空间距离和域失配的两个特征提取器，以增强特征表示。由于针对特征提取器训练了单独的分类器，因此进一步利用集成学习来获得最终结果。实验结果表明，该方法有效且适用于域失配故障的诊断。