Abstract Despite the recent success in data-driven machinery fault diagnosis, cross-domain diagnostic tasks still remain challenging where the supervised training data and unsupervised testing data are collected under different operating conditions. In order to address the domain shift problem, minimizing the marginal domain distribution discrepancy is considered in most of the existing studies. While improvements have been achieved, the class-level alignments between domains are generally neglected, resulting in deteriorations in testing performance. This paper proposes an adversarial multi-classifier optimization method for cross-domain fault diagnosis based on deep learning. Through adversarial training, the overfitting phenomena of different classifiers are exploited to achieve class-level domain adaptation effects, facilitating extraction of domain-invariant features and development of cross-domain classifiers. Experiments on three rotating machinery datasets are carried out for validations, and the results suggest the proposed method is promising for cross-domain fault diagnostic tasks.

中文翻译：

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基于深度学习的对抗性多分类器优化跨域机械故障诊断

摘要 尽管最近在数据驱动的机械故障诊断方面取得了成功，但跨域诊断任务仍然具有挑战性，其中在不同操作条件下收集有监督的训练数据和无监督的测试数据。为了解决域转移问题，大多数现有研究都考虑了最小化边缘域分布差异。虽然已经实现了改进，但域之间的类级对齐通常被忽略，导致测试性能下降。本文提出了一种基于深度学习的跨域故障诊断的对抗性多分类器优化方法。通过对抗训练，利用不同分类器的过拟合现象，达到类级域适应效果，促进域不变特征的提取和跨域分类器的开发。对三个旋转机械数据集进行了实验以进行验证，结果表明所提出的方法有望用于跨域故障诊断任务。