A Fusion Tfdan-Based Framework for Rotating Machinery Fault Diagnosis Under Noisy Labels

Abstract

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.

Keywords: Acoustic-vibration signalContinuous wavelet transformTensor fusionDual attention networkComparative learning