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- Fault diagnosis for wind turbine gearboxes by using deep enhanced fusion networkPublication . Pu, Ziqiang; Li, Chuan; Zhang, Shaohui; Bai, YunThe gearbox will directly affect the safety and reliability of the wind turbine, whose failure leads to low processing accuracy and certain economic losses. To address this issue, a deep enhanced fusion network (DEFN) is proposed for the fault diagnosis of the wind turbine gearbox with the experimental vibration data. In the proposed DEFN, three sparse autoencoders are first applied to extract deep features of three-axial vibration signals, respectively. Second, a feature enhancement mapping is developed to minimize the intraclass distance of the deep features in the three-axial vibration. Finally, the fused three-axis features are put into an echo state network for fault classification. The results of the experiment carried out in a wind turbine show that the proposed DEFN has a good fault diagnosis accuracy compared with other peer models.
- Exploiting graph neural network with one-shot learning for fault diagnosis of rotating machineryPublication . Shuai Yang; Xu Chen; Yu Wang; Yun Bai; Pu, ZiqiangInsufficient training data often leads to overfitting, posing a significant challenge in diagnosing faults in mechanical devices, particularly rotating machinery. To address this issue, this paper introduces a novel approach employing a graph neural network (GNN) with one-shot learning for fault diagnosis in rotating machinery. Firstly, the Short-Time Fourier Transform (STFT) is utilized for data preprocessing to convert the one-dimensional data into two-dimensional pictures. Subsequently, Feature extraction a convolutional neural network (CNN) is utilized to perform feature extraction. By introducing the adjacency matrix to explore the spatial information within data, a graph neural network (GNN) method is proposed to achieve the fault classification of rotating machinery with small sample. The method utilizes GNN to process structural information between, transferring the distance metric from Euclidean space to non-Euclidean space. Classification accuracy is thereby improved based on information processing in non-Euclidean space.Experiments were implemented on two datasets to verify the proposed method, including an open dataset of the rolling bearing and an experimental rig of the rotate vector (RV) reducer in an industrial robot. Siamese Net, Matching Net, and sparse auto-encoder with random forest (SAE + RF) wereemployed as the comparisons to further prove the effectiveness of the proposed method. Results indicate that the proposed method outperforms all the comparative methods in both rotating machineries.