A large amount of labeled data are important to enhance the performance of deep-learning-based methods in the area of fault diagnosis. Because it is difficult to obtain high-quality samples in real industrial applications, federated learning is an effective framework for solving the problem of sparse samples by using the distributed data. Its global model is updated by the local client without sharing data at each round. Considering computing resources and communication loss of multiple clients, an efficient method based on stacked sparse autoencoders (SSAEs) and Siamese networks is proposed to detect interturn short-circuit (ITSC) faults in permanent magnet synchronous motors. In this article, to achieve an accurate ITSC fault detection, an SSAE was employed to extract sparse features in a limited number of samples, and Siamese networks were used to determine the similarity between the given samples. The problem of fault diagnosis is transformed into a classification problem under few-shot learning. Furthermore, the proposed method is trained successfully in the frameworks of centralized learning and decentralized structure. The experimental results indicate that the proposed method achieved high fault diagnosis accuracy. Moreover, it is suitable for deployment in smart manufacturing systems.

Due to the diverse equipment and uneven load distribution in industrial environments, data regarding faults are often unbalanced. Moreover, data and models from clients may become contaminated or damaged, affecting diagnostic performance. To overcome these problems, this study proposes a stacking model for diagnosing interturn short circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs). Federated learning (FL) is used to train the model to increase data security and overcome data islanding in distributed scenarios. Moreover, an improved verification strategy was adopted to select appropriate client models in each round to update the FL global model. We created a secondary server-side data set to validate the client weightings. The data set contains clean sample data for all ITSC fault categories. By calculating the fault diagnosis accuracy of the global model on the auxiliary data set, the model eliminates low-quality clients with uneven fault distributions. The improved particle swarm optimization (PSO) is used to optimize the weight coefficients of clients involved in aggregation, improving the robustness of the aggregation strategy under a joint learning system. In evaluation experiments, compared with the federated average (FedAvg) model, the proposed dynamic verification model exhibited the better diagnostic accuracy in situations of data imbalance, incurred lower communication costs, and prevented local oscillations in the model.

Smart manufacturing, which is increasingly popular worldwide, is aided by time-series forecasting. As the volume of historical data increases, powerful forecasting techniques that reveal unknown relationships between past and future values are required to provide accurate forecasts of production and sales. Thus, in this article, a composite gate recurrent unit (GRU)-Prophet model with an attention mechanism was constructed to predict sales volume. In this composite model, Prophet model and GRU model with attention mechanism were used to capture linear and nonlinear features, respectively. The composite model was experimentally determined to be more applicable and to provide more accurate predictions than did recurrent neural network, long short-term memory, gate recurrent unit, Prophet, and autoregressive integrated moving average models. This article's composite model is suited to rapid changes in market demand and helps enterprises be more competitive in the field of smart manufacturing.

As a vital part of autonomous driving, vehicle detection, especially for outdoor small target vehicles, has attracted great attention from researchers during recent years. To ameliorate the difficulty in accurately identifying outdoor small vehicle targets in dense environments, this paper proposes a new structure named YOLO-SSFS, in which SPD-Conv, a small target detection layer (STDL), the Improved Feature Pyramid Network structure (IM-FPN), and the SCYLLA-IoU (SIoU) loss function are introduced. Firstly, the multi-scale fusion module of the original algorithm is improved by adding a detection layer for smaller targets. This detection layer preserves shallow semantic information, which helps to refine the algorithm’s detection accuracy for small targets. Then, a new Convolutional Neural Network (CNN) building block named SPD-Conv is constructed to replace the pooling layers and convolutional layers in the YOLOv5 algorithm, which reduces information loss, ensures the original fine-grained details of the image and improves the learning ability. Afterwards, a new FPN structure is created to retain more information conducive to small target detection during the feature fusion process so as to enhance the robustness of the method. Finally, to speed up the convergence of the loss function, the SIoU loss function is introduced to replace Complete-IoU (CIoU) in the original algorithm. In order to verify the authenticity of the improved algorithm, we conduct a series of experiments on the VisDrone dataset and perform comparative analyses of the results. The results obtained demonstrate that compared with the original YOLOv5, the proposed model performs better in small target detection. The mean average precision (mAP) is 83.07%, which is 7.63% higher than that for YOLOv5, while the detection speed reaches 52 frames per second (FPS), meeting the requirements for real-time detection.

With the rapid development of artificial intelligence, deep learning has become widely used to monitor the state of permanent magnet synchronous motors (PMSMs). However, to achieve accurate condition detection, datasets with large quantities of labeled data must be used for model training. Accuracy decreases considerably when the quantity of labeled data is insufficient. To address this problem, a fault diagnosis method based on an improved deep Q-network (DQN) is designed in this study to detect inter-turn short circuit (ITSC) faults. In this method, first, a one-dimensional group convolutional neural network–based conditional generative adversarial network is used to expand the collected dataset. Subsequently, a DQN-based fault diagnosis method is applied. A one-dimensional deep residual shrinkage network and the prioritized experience replay strategy are introduced into the original network structure, and the sampling strategy of the original network is optimized. Finally, the fault diagnosis capability of the proposed method was evaluated in several experiments. The proposed method considerably outperformed competing algorithms: it had a diagnosis rate of 98.49% for the ITSC faults of PMSMs and a diagnosis rate of 99.50% on the bearing dataset of Case Western University with certain generalizations. The proposed method is promising for use in circuit fault detection.