Fault Diagnosis for the rolling bearing is an important field that has received increasing attention in recent years. The main challenge for this task is the lack of labeled data. Existing works circumvent this problem with pseudo labels generated from labeled data. However, these pseudo labels are noisy even with consistency check or confidence-based filtering due to the minimal amount of training data. To solve this problem, a novel Label-level Anti-noise Fault Diagnosis Network (LAFD-Net) is proposed in this paper. Specifically, we propose an Online Asymptotic Label Updating (OALU) strategy which contains two updating stages: self-correction stage and cross-correction stage. The proposed OALU can stably and reliably generate new corrected pseudo labels, gradually replacing the old noisy ones. The LAFD-Net adopts a Student-Teacher architecture. For such a Student-Teacher model, we propose a Consistency Enhancement (CE) loss to strengthen the feature consistency between student and teacher networks, aiming to achieve more efficient use of plentiful unlabeled data via feature regularization. Finally, a series of experiments were conducted using the University of Cincinnati Intelligent Maintenance System (IMS) Center dataset and the Case Western Reserve University (CWRU) bearing dataset. The experimental results demonstrate that the proposed semi-supervised learning schemes outperformed existing state-of-the-art methods with the same percentage of labeled data samples.

Effectively learning and extracting the feature representations of 3D point clouds is an important yet challenging task. Most of existing works achieve reasonable performance in 3D vision tasks by modeling the relationships among points appropriately. However, the feature representations are only learned with a specific transform through these methods, which are easy to overlap and thus limit the representation ability of the model. To address these issues, we propose a novel Multi-Transform Learning framework for point clouds (PointMTL), which can extract diverse features from multiple mapping transform to obtain richer representations. Specifically, we build a module named Multi-Transform Encoder (MTE), which encodes and aggregates local features from multiple non-linear transforms. To further explore global context representations, a module named Global Spatial Fusion (GSF) is proposed to capture global information and selectively fuse with local representations. Moreover, to guarantee the richness and diversity of learned representations, we further propose a Spatial Independence Criterion (SIC) strategy to enlarge the differences between the transforms and reduce information redundancies. In contrast to previous works, our approach fully exploits representations from multiple transforms, thus having strong expressiveness and good robustness for point clouds related tasks. The experiments on three typical tasks (i.e., semantic segmentation on S3DIS and ScanNet, part segmentation on ShapeNet and shape classification on ModelNet40) demonstrates the effectiveness of our method.

Soft errors induced by radiation are the major reliability threat for SRAM-based field-programmable gate arrays (FPGAs). A more detailed analysis of the soft error sensitivity of the 40 nm SRAM-based FPGA was performed. Experimental methods for the configurable logic module, configure memory cells, and block RAM have been introduced for measuring the single event effects (SEEs) induced by alpha particles using a ²⁴¹Am radiation source. The single event upset (SEU) and single event functional interrupt (SEFI) cross sections of different functional blocks have been calculated to discuss the failure mechanisms of the FPGA. The SEEs test results for the FPGA device based on the 40 nm CMOS process are significant.

To improve the reliability and maintainability of the nuclear safety-class digital control system (DCS), this paper conducts a study on the fault prediction of critical components in the output circuit of the nuclear safety-class signal conditioning module. To address the issue of insufficient feature extraction for the minor offset fault feature and the low accuracy of fault prediction, a predictive model based on stacked denoising autoencoder (SDAE) feature extraction and an improved gated recurrent unit (GRU) is proposed. Therefore, fault simulation modeling is performed for critical components of the signal output circuit to obtain fault datasets of critical components, and the SDAE model is used to extract fault features. The fault prediction model based on GRU is established, and the number of hidden layers, the number of hidden layer nodes, and the learning rate of the GRU model are optimized using the adaptive gray wolf optimization algorithm (AGWO). The prediction performance evaluation metrics include the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and absolute error (EA), which are used for evaluating the prediction results of models such as the AGWO-GRU model, recurrent neural network (RNN) model, and long short-term memory network (LSTM). The results show that the GRU model optimized by AGWO has a better prediction accuracy (errors range within 0.01%) for the faults of the circuit critical components, and, moreover, can accurately and stably predict the fault trend of the circuit.