A Multi-scale Convolutional Autoencoder with Attention Mechanism for Fault Diagnosis of Rotating Machinery

In recent years, intelligent fault diagnosis based on deep learning has achieved vigorous development due to its powerful feature representation ability. However, the data collected from industrial sites often contain different levels of noise, which makes it difficult to extract effective fault features, which seriously affects the performance of the model. In addition, the limited labeled data makes the training of deep network models even more challenging. To address the above problems, a multi-scale convolutional autoencoder with attention mechanism (MSCAE-AM) is developed. Specifically, as a typical unsupervised learning model, the encoder can effectively reduce the dependence on labeled data. Furthermore, the feature extraction ability of the model in noisy environments can be improved by combining noise reduction operations and embedding multi-scale convolutional layers and attention mechanisms. Experimental results on the wind turbine fault simulation datasets verify the effectiveness and superiority of the proposed method. The results show that the proposed method can not only effectively reduce the dependence on label data, but also has stronger robustness to noise than other methods.