Bearing faults are among the most common causes of machine failures. Therefore, bearing fault diagnosis should be performed reliably and rapidly. Currently, many types of modal data for monitoring the running state of bearings are available. They include data from different kinds of sensors and various domains (such as time and frequency domains). However, obtaining fault features with high quality from single-modal data is difficult because of the complex working conditions. Therefore, extracting and integrating complementary fault features from multimodal monitoring data are problems that remain to be solved. To address these issues, this study considers vibration and torque signals and proposes a novel three-stage feature fusion method of multimodal data for the fault diagnosis of bearings. The method is called attention-based multidimensional concatenated convolutional neural network. The attention mechanism can learn global information and assign different weights to feature maps to highlight important features. The fused features are then used in fault classification via softmax regression. The effectiveness of the proposed method is verified through the Paderborn data set. Results show that diagnostic accuracy is significantly improved from 96.4% to 99.8%.

中文翻译：

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轴承故障诊断的多模态数据三阶段特征融合新方法

轴承故障是机器故障的最常见原因。因此，应可靠，迅速地进行轴承故障诊断。当前，有许多类型的模态数据可用于监视轴承的运行状态。它们包括来自不同种类传感器和各个域（例如时域和频域）的数据。但是，由于工作条件复杂，很难从单模态数据获得高质量的故障特征。因此，从多模式监测数据中提取和整合互补故障特征是有待解决的问题。为了解决这些问题，本研究考虑了振动和转矩信号，并提出了一种新的多模态数据的三阶段特征融合方法，用于轴承故障诊断。该方法称为基于注意力的多维级联卷积神经网络。注意机制可以学习全局信息，并为特征图分配不同的权重以突出显示重要特征。然后通过softmax回归将融合特征用于故障分类。通过Paderborn数据集验证了该方法的有效性。结果表明，诊断准确性从96.4％显着提高到了99.8％。