Machine learning methods are widely used for rolling bearing fault diagnosis. Most of them are based on a basic assumption that training and testing data are adequate and follow the same distribution. However, for bearings working under multiple working conditions, dynamic changes are inevitable and labelled vibration data are usually insufficient. To deal with the issues, a new fault diagnosis method using deformable convolutional neural network (CNN), deep long short-term memory (DLSTM) and transfer learning strategies is designed. Specifically, a model is constructed by integrating deformable CNN, DLSTM and dense layers. Among them, deformable CNN enhances the ability of standard CNNs for local feature extraction using fixed geometric structures. DLSTM further encodes the sequential information contained in the output of deformable CNN. Dense layers are applied to capture high-level features then classify the data samples as each fault type. The model is firstly pre-trained using data samples under one working condition. Then, transfer learning strategies are implemented to fine-tune the pre-trained model utilising very few samples of another working condition, enabling it to identify fault types of bearing under new condition. Experiments are conducted and results show that the presented model yields higher than comparative performance compared with state-of-the-art methods.

机器学习方法广泛用于滚动轴承故障诊断。它们中的大多数是基于训练和测试数据足够且遵循相同分布的基本假设。但是，对于在多种工作条件下工作的轴承，不可避免地会发生动态变化，并且标注的振动数据通常不足。为了解决这些问题，设计了一种新的故障诊断方法，即使用可变形卷积神经网络（CNN），深长短期记忆（DLSTM）和转移学习策略。具体而言，通过整合可变形CNN，DLSTM和密集层来构建模型。其中，可变形CNN增强了使用固定几何结构提取标准CNN进行局部特征提取的能力。DLSTM进一步对可变形CNN输出中包含的顺序信息进行编码。应用密集层来捕获高级特征，然后将数据样本分类为每种故障类型。首先在一个工作条件下使用数据样本对模型进行预训练。然后，实施转移学习策略，以利用很少的另一种工况样本对预训练模型进行微调，从而使其能够识别新工况下的轴承故障类型。进行了实验，结果表明，与最新方法相比，该模型的收益率高于比较性能。实施了转移学习策略，以利用很少的其他工作条件样本对预训练模型进行微调，从而使其能够识别新条件下轴承的故障类型。进行了实验，结果表明，与最新方法相比，该模型的收益率高于比较性能。实施了转移学习策略，以利用很少的其他工作条件样本对预训练模型进行微调，从而使其能够识别新条件下轴承的故障类型。进行了实验，结果表明，与最新方法相比，该模型的收益率高于比较性能。