Rolling bearing fault diagnosis is closely related to the safety of mechanical system. In real-world diagnosis, it is difficult to obtain abundant labeled data due to varying operation conditions, complex working environment and inevitable indirect measurement, which will affect the ability of diagnosing. To tackle this problem, a deep transfer maximum classifier discrepancy method is proposed under few labeled data, which utilizes fully deep learning and transfer learning. Firstly, a batch-normalized long-short term memory (BNLSTM) model which can learn the mapping relationship between two kinds of datasets is designed to generate some auxiliary samples. Then, a transfer maximum classifier discrepancy (TMCD) method, which considers the characteristics of each data type by an adversarial strategy, is applied to align probability distributions of auxiliary samples generated by BNLSTM and unlabeled data from target domain. Sufficient experimental results indicate the effectiveness of the proposed method under few labeled data.

滚动轴承的故障诊断与机械系统的安全性息息相关。在实际诊断中，由于操作条件变化，工作环境复杂，不可避免的间接测量等原因，难以获得丰富的标记数据，这将影响诊断能力。为了解决这个问题，提出了一种在少量标记数据下的深度转移最大分类器差异方法，该方法充分利用了深度学习和转移学习的特点。首先，设计了一种可以学习两种数据集之间映射关系的批标准化长短期记忆（BNLSTM）模型，以生成一些辅助样本。然后，采用最大转移分类器差异（TMCD）方法，通过对抗策略考虑每种数据类型的特征，应用于对齐BNLSTM生成的辅助样本与目标域中未标记数据的概率分布。足够的实验结果表明，该方法在少量标记数据下是有效的。