Accurate remaining useful life (RUL) prediction can formulate timely maintenance strategies for mechanical equipment and reduce the costs of industrial production and maintenance. Although data-driven methods represented by deep learning have been successfully implemented for mechanical equipment RUL prediction, existing methods generally require test data to have a similar distribution to the training data. Due to the domain shift problem caused by the changes in equipment operating conditions and fault types, the previously trained models lose the accuracy of prediction under the new conditions. In response to this problem, we combined a deep learning model with domain adaptation and proposed a Wasserstein distance based multi-scale adversarial domain adaptation (WD-MSADA) method for complex machinery RUL prediction. The proposed WD-MSADA utilizes a discriminator to compute the Wasserstein distance to guide the adversarial domain adaptation process more stably to reduce the distribution differences between the source and target domains. Additionally, a multi-scale convolutional neural network (MSCNN) is proposed as a feature extractor to learn the common multi-scale features between two domains, improving the shortcomings of traditional CNNs and enhancing the domain adaptation capability. Experiments on the RUL prediction of turbofan engines in 12 cross-domain scenarios demonstrate that the proposed WD-MSADA performs reliable RUL prediction in unlabeled target domains, and the prediction results are compared with models without domain adaptation, other advanced domain adaptation methods, and the model without multi-scale, demonstrating the superiority and reliability of the proposed method.

准确的剩余使用寿命（RUL）预测可以为机械设备制定及时的维护策略，降低工业生产和维护成本。虽然以深度学习为代表的数据驱动方法已成功应用于机械设备 RUL 预测，但现有方法通常要求测试数据与训练数据具有相似的分布。由于设备运行条件和故障类型的变化引起的域转移问题，先前训练的模型在新条件下失去了预测的准确性。针对这一问题，我们将深度学习模型与域适应相结合，提出了一种基于 Wasserstein 距离的多尺度对抗域适应（WD-MSADA）方法，用于复杂机械 RUL 预测。所提出的WD-MSADA利用判别器计算Wasserstein距离，以更稳定地引导对抗域适应过程，以减少源域和目标域之间的分布差异。此外，提出了一种多尺度卷积神经网络（MSCNN）作为特征提取器来学习两个域之间的共同多尺度特征，改善了传统CNN的缺点并增强了域适应能力。涡扇发动机在 12 个跨域场景下的 RUL 预测实验表明，所提出的 WD-MSADA 在未标记的目标域中进行了可靠的 RUL 预测，并将预测结果与没有域自适应的模型、其他高级域自适应方法以及没有多尺度的模型，