Abstract As the essential components of rotating machines, rolling bearings always operate in variable working conditions and suffer from different failure modes. To address the issue of lacking substantial labeled samples in new working conditions, a domain adaptive deep belief network (DA-DBN) is proposed for rolling bearing fault diagnosis. Firstly, the DBN model is pre-trained by the labeled samples which are composed of raw vibration signals and their corresponding time domain and frequency domain indicators. Secondly, the domain adaption method in transfer learning is applied to calculate the multi-kernel maximum mean discrepancies (MK-MMD) between the known working condition data and new working condition data in multiple layers. Thus, the loss function composed of MK-MMD and classification error can be obtained, and back propagation algorithm is used to fine-tune model parameters. Finally, the datasets with five fault patterns are collected to evaluate the performance of the DA-DBN. The results demonstrate that the proposed DA-DBN can achieve more than 92% fault classification accuracy under three noise levels; the average accuracy of fault classification under variable working conditions is 93.5%, which is the highest compared with other models.

中文翻译：

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用于滚动轴承故障诊断的域自适应深度置信网络

摘要 滚动轴承作为旋转机械的重要组成部分，总是在多变的工况下运行，遭受不同的故障模式。为了解决新工作条件下缺乏大量标记样本的问题，提出了一种用于滚动轴承故障诊断的域自适应深度置信网络（DA-DBN）。首先，由原始振动信号及其相应的时域和频域指标组成的标记样本对DBN模型进行预训练。其次，应用迁移学习中的域自适应方法计算多层已知工况数据和新工况数据之间的多核最大平均差异（MK-MMD）。这样就可以得到由MK-MMD和分类误差组成的损失函数，反向传播算法用于微调模型参数。最后，收集具有五种故障模式的数据集来评估 DA-DBN 的性能。结果表明，所提出的DA-DBN在三种噪声水平下都能达到92%以上的故障分类准确率；变工况下故障分类的平均准确率为93.5%，是其他机型中最高的。