Traditional fault diagnosis (FD) for rotating machinery solely based on vibration signals has problems such as inconvenient collection, low accuracy, and poor robustness. This article proposes a fusion framework based on tensor fusion and dual attention network (TFDAN), utilizing acoustic and vibration signals from two datasets of centrifugal pumps and cylindrical roller bearings. Firstly, continuous wavelet transform (CWT) is used to transform two original signals into two-dimensional time–frequency maps to highlight time–frequency features. Then, the images are fed into the fusion framework, where tensor fusion can construct the corresponding time–frequency maps of the working conditions into multi-channel datasets, enhancing the feature connections between acoustic and vibration signals. The dual attention network takes on the fused samples, extracts local features of the image using its positional attention module, and further aggregates feature correlations between channels using its channel attention mechanism. Finally, in order to simulate the actual production situation, different proportions of noisy labels are added to the dataset. In response to the impact of noisy labels, we incorporate an improved contrastive regularization function (ICRF) into the model, fully utilizing its advantage of preventing overfitting of noisy labels. The effectiveness of our proposed method has been demonstrated through two experimental cases. Compared with other methods, our method has better performance in terms of diagnostic accuracy and robustness to noisy labels.

中文翻译：

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基于融合TFDAN的噪声标签下旋转机械故障诊断框架

传统仅基于振动信号的旋转机械故障诊断（FD）存在采集不便、精度低、鲁棒性差等问题。本文提出了一种基于张量融合和双重注意网络（TFDAN）的融合框架，利用来自离心泵和圆柱滚子轴承两个数据集的声学和振动信号。首先，使用连续小波变换（CWT）将两个原始信号变换为二维时频图，以突出时频特征。然后，将图像输入融合框架，其中张量融合可以将工作条件的相应时频图构建为多通道数据集，增强声学和振动信号之间的特征联系。双注意网络采用融合样本，使用其位置注意模块提取图像的局部特征，并使用其通道注意机制进一步聚合通道之间的特征相关性。最后，为了模拟实际生产情况，在数据集中添加不同比例的噪声标签。针对噪声标签的影响，我们在模型中加入了改进的对比正则化函数（ICRF），充分利用其防止噪声标签过拟合的优势。我们提出的方法的有效性已通过两个实验案例得到证明。与其他方法相比，我们的方法在诊断准确性和对噪声标签的鲁棒性方面具有更好的性能。