When a series arc fault occurs in indoor power distribution system, current value of circuit is often less than the threshold of the circuit breaker, but the temperature of arc combustion can be as high as thousands of degrees, which can lead to electrical fire. The arc fault experimental platform is used to collect circuit current data of normal work and arc fault. Five types of loads which are commonly used in indoor distribution system, such as resistive and inductive in series load, resistive load, series motor load, switching power load and eddy current load, are chosen. This paper uses four features of current in time domain, i.e. current average, current pole difference, difference current average and current variance. Ten features of current in frequency domain are extracted by Fast Fourier Transform (FFT). The learning vector quantization neural network (LVQ-NN) is designed to judge the load type. The support vector machine optimized by particle swarm optimization (PSO-SVM) is designed to detect the arc fault. The simulation results show the effectiveness of the proposed method.

Insulated conductors can improve the stability of power transmission and reduce the construction space compared with traditional bare conductors. Therefore, insulated conductors are used more and more in overhead power transmission. However, a major challenge of using insulated overhead conductors (IOC) is that the ordinary protection devices are not able to detect the phase-to-ground faults and something, such as tree branch, hitting conductor events. This may cause an accident such as power failure or electrical fire and result in serious damage. In this paper, a new approach, which is based on Discrete Wavelet Transform (DWT) and Long Short Term Memory network (LSTM) for detecting of IOC fault according to partial discharge, is presented. Firstly, the original signal is denoised by DWT. Secondly, the denoised signal is decomposed and extracted features on different layers by DWT. Finally, IOC fault is detected by LSTM. This method can improve the detection accuracy of IOC fault which is tested on the ENET public data set and compared with other classification methods.

In recent years, intelligent fault diagnosis technology with deep learning algorithms has been widely used in industry, and they have achieved gratifying results. Most of these methods require large amount of training data. However, in actual industrial systems, it is difficult to obtain enough and balanced sample data, which pose challenges in fault identification and classification. In order to solve the problems, this paper proposes a data generation strategy based on Wasserstein generative adversarial network and convolutional neural network (WG-CNN), which uses generator and discriminator to conduct confrontation training, expands a small sample set into a high-quality dataset, and uses one-dimensional convolutional neural network (1D-CNN) to learn sample characteristics and classify different fault types. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that the proposed method has obvious and satisfactory fault diagnosis effect with 100% classification accuracy for few-shot learning. In different noise environments, this method also has excellent performance.

Compared with traditional model-based fault detection and classification (FDC) methods, deep neural networks (DNN) prove to be more accurate for the aerospace sensors FDC problems. A concept known as imagefication-based intelligent FDC has been studied in recent years. This concept advocates to stack the sensors measurement data into an image format, the sensors FDC issue is transformed to abnormal regions detection problems on the stacked image. Although promising advances have been claimed, due to low size of the stacked image, small convolutional kernels and shallow DNN layers were used, the FDC performances were hindered. In this paper, we propose a data augmentation method which inflates the stacked image to a larger size (corresponding to the VGG16 object detection net in the machine vision realm). The FDC neural network is then trained via fine-tuning the VGG16. To compress the net size, layered channel pruning algorithm evaluates the importance of the convolutional filters enclosed in the net, channel pruning with knowledge distillation algorithm is then implemented, which yields a more lightweight FDC neural network. The fusion of Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) methods are adopted for explainability analysis of the FDC net, which clarifies its internal mathematical operations both locally (e.g. convolutions) and globally (e.g. deep Shaley values). Via data augmentation, fine-tuning from VGG16, and pruning, the FDC net developed in this paper claims an accuracy of 98.99% across 4 aircraft at 5 flight conditions. We also deploy the FDC net on real data from the flight of a model airplane, which claims an accuracy of 98.44%. Both high accuracy and explainability analysis presented in this paper justifies the efficacy of the proposed scheme.

Zhong-Zhi Li, Ming-Liang Zhang, Fei Yin, Cheng-Lin Liu. Findings of the Association for Computational Linguistics ACL 2024. 2024.

Congenital heart disease (CHD) is the most common developmental anomaly, affecting approximately 1% of all newborns. Genetic factors play an important role in CHD's development. Germline mutations in NK2 transcription factor related, locus 5 (NKX2.5) have been identified as the factors responsible for various forms of CHD. In this study, we investigated mutations of the NKX2.5 gene's coding region in 230 nonsyndromic CHD patients belonging to the Chinese Han nationality by PCR, denaturing high-performance liquid chromatography, and sequencing. Pathogenic mutations were not found among the patients. Two known single-nucleotide polymorphisms (rs2277923 and rs3729753) were detected, but the differences in the allele and genotype frequencies were insignificant between CHD and the controls (p > 0.05). The data we gathered suggest that NKX2.5 mutations are highly rare in CHD patients of the Chinese Han nationality. Therefore, NKX2.5 mutation investigation should be limited within a number of familial and special phenotype of CHD in Chinese patients.