In recent years, deep learning methods have gained much success in the area of intelligent fault diagnosis. However, due to the fact that the working condition is various and the noise is inevitable, degradation of previous model is very serious. To address the challenge of bearing fault detection under strong noise environment, this paper proposed a novel anti-noise deep residual multiscale convolutional neural network with attention mechanism named Attention-MSCNN. First, dynamic dropout is used to improve the anti-noise ability by introducing artificial noise into training process. Additionally, we design a residual connection between input and the convolved features to fully capture the characteristics of the initial input. Finally, a novel denoised multi-head attention mechanism is applied to remove excess noise in raw input and obtain relationships between long time series. The experimental results show that Attention-MSCNN can achieve robust anti strong noise performance with over 85% accuracy on CWRU dataset. On the self collected two-stage gear drive test bench, our model achieve an accuracy of over 99% under strong noise environment. Thus, Attention-MSCNN successfully solve the problem of low detection accuracy of previous models under strong noise environment.

This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals. In our approach, to extract more effective features from a raw signal, a novel deep convolutional neural network combining global feature extraction with detailed feature extraction (GDDCNN) is proposed. First, wide and small kernel sizes are separately adopted in shallow and deep convolutional layers to extract global and detailed features. Then, the modified activation layer with a concatenated rectified linear unit (CReLU) is added following the shallow convolution layer to improve the utilization of shallow global features of the network. Finally, to acquire more robust features, another strategy involving the GMP layer is utilized, which replaces the traditional fully connected layer. The performance of the obtained diagnosis was validated on two bearing datasets. The results show that the accuracy of the compound fault diagnosis is over 98%. Compared with three other CNN-based methods, the proposed model demonstrates better stability.

In recent years, deep learning has shown significant potential in bearing fault diagnosis. However, challenges remain, including suboptimal signal quality under intricate conditions and the impact of network architecture on model performance. This study proposes a joint feature extraction method that combines a multi-scale residual convolutional neural network with a position-encoded transformer and integrating a transfer learning (TL) strategy to address the aforementioned issues. First, multi-scale convolutional layers are utilized to capture the details and local features from raw signals. To complement this, a transformer with embedded positional encoding learns global dependencies while retaining position information, effectively compensating the position shift issue. To further enhance the model’s generalization capability, a data augmentation strategy is designed and implemented, diversifying the training data. Furthermore, a TL strategy predicated on model fine-tuning is applied to mitigate the reliance on a substantial quantity of labeled data. Experiments conducted on two datasets, including Case Western Reserve University (CWRU) dataset and Self-Collected two-stage gear drive test bench dataset, that featuring diverse working conditions and bearing types. The proposed model achieved 99.92% accuracy on the CWRU dataset and demonstrated strong robustness across different operating conditions. With a model size of approximately 1.45MB, it maintains high diagnostic performance while ensuring computational efficiency. This intelligent diagnosis technique can also be applied to other rotating machinery, such as wind power, locomotive and various other fields owing to its robust feature extraction abilities.

In the area of bearing fault diagnosis, deep learning (DL) methods have been widely used recently. However, due to the high cost or privacy concerns, high-quality labeled data are scarce in real world scenarios. While few-shot learning has shown promise in addressing data scarcity, existing methods still face significant limitations in this domain. Traditional data augmentation techniques often suffer from mode collapse and generate low-quality samples that fail to capture the diversity of bearing fault patterns. Moreover, conventional convolutional neural networks (CNNs) with local receptive fields makes them inadequate for extracting global features from complex vibration signals. Additionally, existing methods fail to model the intricate relationships between limited training samples. To solve these problems, we propose an advanced data augmentation and contrastive fourier convolution framework (DAC-FCF) for bearing fault diagnosis under limited data. Firstly, a novel conditional consistent latent representation and reconstruction generative adversarial network (CCLR-GAN) is proposed to generate more diverse data. Secondly, a contrastive learning based joint optimization mechanism is utilized to better model the relations between the available training data. Finally, we propose a 1D fourier convolution neural network (1D-FCNN) to achieve a global-aware of the input data. Experiments demonstrate that DAC-FCF achieves significant improvements, outperforming baselines by up to 32\% on case western reserve university (CWRU) dataset and 10\% on a self-collected test bench. Extensive ablation experiments prove the effectiveness of the proposed components. Thus, the proposed DAC-FCF offers a promising solution for bearing fault diagnosis under limited data.