A Multistage Deep Transfer Learning Method for Machinery Fault Diagnostics Across Diverse Working Conditions and Devices

Deep learning methods have promoted the vibration-based machinery fault diagnostics from.manual feature extraction to an end-to-end solution in the past few years and exhibited great success on.various diagnostics tasks. However, this success is based on the assumptions that sufcient labeled data.are available, and that the training and testing data are from the same distribution, which is normally.difcult to satisfy in practice. To overcome this issue, we propose a multistage deep convolutional transfer.learning method (MSDCTL) aimed at transferring vibration-based fault diagnostics capabilities to new.working conditions, experimental protocols and instrumented devices while avoiding the requirement for.new labeled fault data. MSDCTL is constructed as a one-dimensional convolutional neural network (CNN).with double-input structure that accepts raw data from different domains as input. The features from different.domains are automatically learned and a customized layer is designed to compute the distribution discrepancy.of the features. This discrepancy is further minimized such that the features learned from different domains.are domain-invariant. A multistage training strategy including pre-train and ne-tuning is proposed to.transfer the weight of a pre-trained model to new diagnostics tasks, which drastically reduces the requirement.on the amount of data in the new task. The proposed model is validated on three bearing fault datasets from.three institutes, including one from our own. We designed nine transfer tasks covering fault diagnostics.transfer across diverse working conditions and devices to test the effectiveness and robustness of our model..The results show high diagnostics accuracies on all the designed transfer tasks with strong robustness..Especially for transfer to new devices the improvement over state of the art is very signicant.

在过去几年中，深度学习方法推动了基于振动的机械故障诊断从手动特征提取向端到端解决方案发展，并在各种诊断任务中取得了巨大成功。然而，这种成功是基于有足够的标记数据可用，以及训练数据和测试数据来自相同分布的假设，而在实践中这些通常很难满足。为了克服这个问题，我们提出了一种多级深度卷积迁移学习方法（MSDCTL），旨在将基于振动的故障诊断能力迁移到新的工作条件、实验方案和仪器设备中，同时避免对新的标记故障数据的需求。MSDCTL被构建为一个具有双输入结构的一维卷积神经网络（CNN），它接受来自不同域的原始数据作为输入。来自不同域的特征被自动学习，并且设计了一个定制层来计算特征的分布差异。这种差异被进一步最小化，使得从不同域学习到的特征具有域不变性。提出了一种包括预训练和微调的多级训练策略，以将预训练模型的权重迁移到新的诊断任务中，这大大减少了对新任务中数据量的要求。所提出的模型在来自三个机构（包括我们自己的一个机构）的三个轴承故障数据集上进行了验证。我们设计了九个迁移任务，涵盖了不同工作条件和设备之间的故障诊断迁移，以测试我们模型的有效性和鲁棒性。结果显示，在所有设计的迁移任务上都有很高的诊断准确率，并且具有很强的鲁棒性。特别是对于迁移到新设备，与现有技术相比有非常显著的改进。