In current research works, a number of intelligent fault diagnosis methods have been proposed with the assistance of domain adaptation approach, which attempt to distinguish the health modes for target domain data using the diagnostic knowledge learned from source domain data. An important assumption for these methods is that the label information for the source domain data should be known in advance. However, the high-quality condition monitoring data with sufficient label information is difficult to be acquired in the actual field, which can greatly hinder the effectiveness of domain adaptation–based fault diagnosis methods. The simulation model of the rotating machine is an effective approach to provide an insight into the characteristics of the mechanical equipment, which can also easily carry the sufficient label information for the mechanical equipment under various operating conditions. In this study, a simulation data–driven domain adaptation approach is proposed for the intelligent fault diagnosis of mechanical equipment. The simulation data from a rotor-bearing system are used to build the source domain data set, and the diagnostic knowledge learned from the simulation data is used to realize the healthy mode identification of mechanical equipment in the actual field. The proposed domain adaption approach consists of two parts. The first part is to achieve the conditional distribution alignment between source domain data and target domain supervised data in an alternative way. The second part is to achieve the marginal distribution alignment between source domain data and target domain unsupervised data in an adversarial training process. The proposed domain adaptation method is evaluated on two case studies, the diagnostic results on two case studies indicate that the proposed domain adaptation method is capable of realizing the fault diagnosis of mechanical equipment using the diagnostic knowledge learned from simulation data.

在当前的研究工作中，已经提出了多种智能的故障诊断方法，这些方法都借助于域自适应方法，试图利用从源域数据中学到的诊断知识来区分目标域数据的健康模式。这些方法的重要假设是，应事先知道源域数据的标签信息。但是，在实际领域中很难获得带有足够标签信息的高质量状态监视数据，这可能大大阻碍基于域自适应的故障诊断方法的有效性。旋转机械的仿真模型是一种有效的方法，可以深入了解机械设备的特性，在各种操作条件下，还可以轻松携带足够的机械设备标签信息。在这项研究中，提出了一种模拟数据驱动的域自适应方法，用于机械设备的智能故障诊断。利用来自转子轴承系统的仿真数据建立源域数据集，并利用从仿真数据中学到的诊断知识来实现​​机械设备在实际领域中的健康模式识别。拟议的领域适应方法包括两部分。第一部分是以替代方式实现源域数据和目标域监督数据之间的条件分布对齐。第二部分是在对抗训练过程中，实现源域数据和目标域非监督数据之间的边际分布对齐。在两个案例研究中对所提出的领域自适应方法进行了评估，在两个案例研究中的诊断结果表明，所提出的领域自适应方法能够利用从模拟数据中学到的诊断知识来实现​​机械设备的故障诊断。