Deep learning has been widely applied to intelligent fault diagnosis with balanced training set. However, certain available fault data are extremely limited, resulting in an imbalanced training set in most cases inevitably. In general, the performance of the deep learning-based diagnosis methods will deteriorate on the imbalance dataset. To solve the problem, a novel dual-attention generative adversarial network (DAGAN) is proposed for dealing with imbalanced fault diagnosis. Firstly, an attention model is constructed to selectively enhance the features at each position and adaptively fuse the interdependent channel maps. Then, the attention model is embedded into generative adversarial network (GAN) to capture the informative features of inputs and improve feature representations. As such, the DAGAN can learn the fault-related features effectively and generate sufficient fault samples. Finally, the diagnosis model can be trained on the rebalanced dataset to improve its classification performance under class-imbalance conditions. Two different datasets are used to validate the proposed method, and the effects of the multiple imbalance ratios on classification performance are discussed. Results show that the proposed method achieves high diagnosis accuracy and outperforms other methods.

Fault diagnosis is of importance to guarantee the printing quality and to avoid unexpected downtime for 3-D printers. In this paper, a sparse echo autoencoder network (SEAEN) is proposed for the fault diagnosis of delta 3-D printers using attitude data. Considering the practicality and economy of the fault diagnosis, the attitude data, including three-axial angular velocity signals, three-axial vibratory acceleration ones, and three-axial magnetic field intensity ones, are collected by installing a low-cost attitude sensor on the moving platform of the delta 3-D printer. However, the low-cost sensor will increase the chaos of the attitude data. To make up this deficiency, the SEAEN approach featuring a sparse autoencoder (SAE) combined with an echo state network (ESN) is designed for the fault diagnosis. The SAE is employed to automatically learn features from the high-dimensional attitude data of the delta 3-D printer, while the ESN is used for fault recognition based on the extracted features. The diagnosis performance of the address approach was evaluated in the experiments and its superiority was demonstrated through comparing with other intelligent fault diagnosis techniques.

Recent diagnostic approaches based on the deep learning model have attracted much attention. However, developing an outstanding AI diagnostic model requires many training samples with labeled information. Moreover, training deep models is labor-intensive and time-consuming, and labeling samples and training models increase workload. To overcome these problems, this article proposes an unsupervised deep transfer learning (DTL) method with an isolation forest (iForest) for machine fault diagnosis. First, the isolation forest is used to classify and label the samples automatically; then, these labeled data are used to train deep learning (DL) models; finally, small data with the label of the target domain are used to fine-tune parameters and complete the fault diagnosis. The proposed approach has been validated with the fan gearbox dataset, the bearing dataset, and the ball screw dataset. The results show that the proposed unsupervised deep transfer learning model has high accuracy and generality.