Fault Detection In Networks Research Articles

End to end machine learning for fault detection and classification in power transmission lines

This paper proposes a new machine learning approach for fault detection and classification tasks in electrical power transmission networks. This method exploits the temporal sequence of the power system's operational data and develops an ‘end to end’ model employing Long Short-Term Memory (LSTM) units working directly on the operational data instead of features. The temporal sequences are different in the case of normal and faulty conditions. End to end learning simplifies the decision-making process and eliminates the need for complex feature extraction process by learning directly from the labelled datasets. The method is rigorously tested for all types of faults, which are further subjected to a range of fault resistance, distance, loading conditions, system parameters and noise levels. The proposed method can also work under power swing conditions. The method is also tested on WSCC 9 bus system. The proposed method has shown fast response in terms of time performance and is resilient towards operational conditions.

Weakly Supervised Surface Defect Detection Based on Attention Mechanism

<p indent=0mm>Nowadays, many defect detection algorithms based on deep learning are trained using a supervised learning strategy, which largely depends on the number of required samples and the quality of annotations. A weakly supervised attention network for surface defect detection is proposed, named SDD-Net, which can simultaneously predict both the location and probability of defects only by using image-level labels. Firstly, the feature fusion module is used to extract multi-scale edges features from the output of the multi-scale receptive field. Then, the deep semantic information of features is mined by multilevel auto-coder. Meanwhile, the trilinear global context attention module is used to further refine the spatial location information of shallow features. Finally, the SDD-Net is used to integrate the shallow edge features and deep semantic features to obtain the final fine defect features. The results of evaluation on KolektorSDD dataset demonstrate that the SDD-Net based on the PyTorch framework has better detection performance than other detection methods such as U-Net. It can retain more detailed texture information and effectively expand the feature difference between small defects and complex background. The experimental results show that the proposed model is more accurate than the other models in the complex scene, the precision, the <italic>F</italic>₁-score and the classification accuracy are significantly improved.

Data Augmentation Using DCGAN for Improved Fault Detection of High Voltage Shunt Reactor

High voltage shunt reactor is an important equipment of power transmission systems. The accurate assessment of their operating status and the timely and correct diagnosis of faults and defects concern the operation safety of the entire grid. Health assessment of high voltage shunt reactors based on vibration signal, which can be used to characterize the hidden troubles of it, is a topic widely studied in deep learning and fault diagnosis. A large number of samples are needed to train the deep learning model, but it is not easy to acquire enough fault samples in the actual scene. In this paper, we utilize a Deep Convolutional Generative Adversarial Networks (DCGAN) to generate synthetic fault samples and enlarge the fault dataset to train the Convolution Neural Network (CNN) fault detection model. Results reveal that the performance through the CNN model can be improved by 3% with the synthetic samples generated by DCGAN, which is better than that of traditional Synthetic Minority Oversampling Technique (SMOTE) algorithm.

Defect Detection of Micro-Precision Glass Insulated Terminals

Micro-precision Glass Insulated Terminals (referred to as glass terminals) are the core components used in precision electronic equipment and are often used for electrical connections between modules. As a glass terminal, its quality has a great influence on the performance of precision electronic equipment. Due to the limitations of materials and production processes, some of the glass terminals produced have defects, such as missing blocks, pores and cracks. At present, most of the defect detection of glass terminals is done by manual inspection, and rapid detection easily causes eye fatigue, so it is difficult to ensure product quality and production efficiency. The traditional defect detection technology is difficult to effectively detect the very different defects of the glass terminal. Therefore, this paper proposes to use deep learning technology to detect missing blocks. First, preprocess the sample pictures of the missing block defects of the glass terminal, and then train the improved Faster Region-CNN deep learning network for defect detection. According to the test results, the accuracy of the algorithm in detecting missing defects in the glass terminal is as high as 93.52%.

An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks

Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy. Wireless sensor networks (WSN) has become the key supporting platform of Internet of Things (IoT). The correctness of the data collected by the sensor nodes has a great influence on the reliability, real-time performance and energy saving of the entire network. The optimized machine learning technology scheme given in this paper can effectively identify the fault data, so as to ensure the effective operation of WSN.

DLSE-Net: A robust weakly supervised network for fabric defect detection

The feasibility of deep convolutional neural network for fabric defect detection has been proven, but the detection performance often depends on large-scale labeled datasets. However, it is troublesome to collect large amounts of fabric defects with pixel-level labeling in industrial production. Although the weakly supervised detection methods can reduce the labeling workload, fabric defect detection is still a challenging task due to the slight difference between defects and complex texture backgrounds, and the diversity of defect types. To alleviate this issue, this paper proposes an effective weakly supervised shallow network, called DLSE-Net, with Link-SE (L-SE) module and Dilation Up-Weight CAM (DUW-CAM) for fabric defect detection. Firstly, the network regards a residual connection as a new branch to alleviate the semantic gap generated by the connection of different layers. Secondly, L-SE module forces the weights to be associated with the overall network in a global optimization manner instead of only within a single layer. Finally, a novel DUW-CAM with an attention mechanism is proposed to improve the adaptability of the network by combining dilated convolution and attention mechanism. Moreover, DUW-CAM can effectively suppress the background and highlight defect regions, even on complex fabric textures. Experimental results demonstrate that our proposed approach can localize the defects with high accuracy, and outperforms the state-of-the-art methods on two distinctive fabric datasets with different textures.

Soft matching network with application to defect inspection

Defect detection with templates is a major concern in manufacturing. Including subtraction and template matching, traditional methods based on images stumbled over the diverse disparities of input image pair. Yet, learning-based approaches have not been explored on this task. This paper proposed a learning-based soft template matching network for defect detection, using an innovative attention mechanism.Employing feature-pyramid-network-based atrous convolution enables our model to perceive multi-scale features. The proposed contrastive attention module enhances the query feature map. Experimental results demonstrate that our network can capture defects under the interference of disparities based on the correspondence of input image pair, showing practical value for industrial defect detection.

ASAFPN:An End-to-End Defect Detector With Adaptive Spatial Attention Method

Industrial defect inspection aims to detect unqualified products from those samples under detection, which plays a vital role in ensuring the quality of products. In this paper, an end-to-end defect detection network is proposed to locate the defect position of steel plate images and predict the defect category. In the detection network, we propose a novel feature pyramid network module, which is called the adaptive spatial attention feature pyramid. It can effectively fuse the texture features at low levels with semantic features at high levels. Based on the proposed module, persuasive experiments are carried out using different backbone networks. Experimental data shows that the feature pyramid module can effectively extract different levels of information and adaptively fuse multiple hierarchical features to improve the detection effect of the network.

Proposed Application for Rate of Change of Phasor Voltage in Fault Detection and Coordination Studies in MV Distribution Networks

Proposed Application for Rate of Change of Phasor Voltage in Fault Detection and Coordination Studies in MV Distribution Networks

An automated fault detection system for communication networks and distributed systems

Automating fault detection in communication networks and distributed systems is a challenging process that usually requires the involvement of supporting tools and the expertise of system operators. Automated event monitoring and correlating systems produce event data that is forwarded to system operators for analyzing error events and creating fault reports. Machine learning methods help not only analyzing event data more precisely but also forecasting possible error events by learning from existing faults. This study introduces an automated fault detection system that assists system operators in detecting and forecasting faults. This system is characterized by the capability of exploiting bug knowledge resources at various online repositories, log events and status parameters from the monitored system; and applying bug analysis and event filtering methods for evaluating events and forecasting faults. The system contains a fault data model to collect bug reports, a feature and semantic filtering method to correlate log events, and machine learning methods to evaluate the severity, priority and relation of log events and forecast the forthcoming critical faults of the monitored system. We have evaluated the prototyping implementation of the proposed system on a high performance computing cluster system and provided analysis with lessons learned.

Effect of Distributed Photovoltaic Generation on Short-Circuit Currents and Fault Detection in Distribution Networks: A Practical Case Study

The increase in the installation of renewable energy sources in electrical systems has changed the power distribution networks, and a new scenario regarding protection devices has arisen. Distributed generation (DG) might produce artificial delays regarding the performance of protection devices when acting as a result of short-circuits. In this study, the preliminary research results carried out to analyze the effect of renewable energy sources (photovoltaic, wind generation, etc.) on the protection devices of a power grid are described. In order to study this problem in a well-defined scenario, a quite simple distribution network (similar to the ones present in rural areas) was selected. The distribution network was divided into three protection zones so that each of them had DG. In the Institute of Electrical and Electronic Engineers (IEEE) system 13 bus test feeder, the short-circuits with different levels of penetration were performed from 1 MVA to 3 MVA (that represent 25%, 50%, and 75% of the total load in the network). In the simulations carried out, it was observed that the installation of DG in this distribution network produced significant changes in the short-circuit currents, and the inadequate performance of the protection devices and the delay in their operating times (with differences of up to 180% in relation to the case without DG). The latter, that is, the impacts of photovoltaic DG on the reactions of protection devices in a radial distribution network, is the most relevant outcome of this work. These are the first results obtained from a research collaboration framework established by staff from ETSI Civil and the IDR/UPM Institute, to analyze the effect of renewable energy sources (as DG) on the protection devices of a radial distribution network.

Multiscale Adversarial and Weighted Gradient Domain Adaptive Network for Data Scarcity Surface Defect Detection

Surface defect detection is a challenging task in industrial manufacture. Recent methods using supervised learning need a large-scale dataset to achieve precise detection. However, the time-consuming and the difficulty of data acquisition make it difficult to build a large-scale dataset. This article proposes a domain adaptive network, called multiscale adversarial and weighted gradient domain adaptive network (MWDAN) for data scarcity surface defect detection. By MWDAN, the detection model trained from a small-scale dataset can gain the knowledge of transfer from another large-scale dataset, that is to say, even for a training dataset that is difficult to collect huge amounts of data, a good defect detection model can also be constructed, aided by another dataset that is relatively easy to acquire. The MWDAN is constructed in two levels. In the image level, a multiscale domain feature adaptation approach is proposed to solve the domain shift between the source domain and the target domain. In the instance level, a piecewise weighted gradient reversal layer (PWGRL) is designed to balance the weight of the backpropagation gradient for the hard- and easy-confused samples in domain classification and force confusion. Then, the PWGRL can reduce the local instance difference to further promote domain consistency. The experiments on mental surface defect detection show encourage results by the proposed MWDAN method.

SDDNet: A Fast and Accurate Network for Surface Defect Detection

This article proposes a fast and accurate network for surface defect detection, termed SDDNet. SDDNet mainly addresses two challenging issues—large texture variation and small size of defects—by introducing two modules: feature retaining block (FRB) and skip densely connected module (SDCM). FRB fuses multiple pyramidal feature maps with different resolutions and is plugged on the top of pooling layers, aiming to preserve the texture information, which may be lost because of downsampling. SDCM is designed to propagate the fine-grained details from low- to high-level feature maps for better prediction of defects, especially small defects. Extensive experiments conducted on the publicly available data sets NEU-DET (88.8% mAP), DAGM (99.1% mAP), and Magnetic-Tile (93.4% mAP) have demonstrated the effectiveness of the proposed SDDNet and its feasibility for real-time industrial applications.

DefectNet: Toward Fast and Effective Defect Detection

The existing object detection algorithms based on the convolutional neural network (CNN) are always devoted to the detection of natural objects and have achieved admirable detection effects. At present, these detection algorithms have been applied to the detection of defect data. In fact, the detection of defect data is different from the detection of general natural object data. First, it has a large number of images without annotations (that is, normal images), and they each contain different background information. Second, its processing principles are fundamentally different from general object detection problems. Therefore, the application of a general object detection algorithm based on CNN may not be perfect in this problem. In this article, a novel defect detection network (DefectNet) is proposed to solve the problem of defect detection. It first uses a shared weight binary classification network to determine whether an image contains the targets and then uses the detection network to detect the targets. Theoretical deduction and experimental results fully confirm that it can effectively improve the detection speed and effect of the general object detection network based on CNN. (Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/li-phone/DefectNet.git</uri> )

Effective Defect Detection Method Based on Bilinear Texture Features for LGPs

Automatic defect detection of light guide plates (LGPs) is an important task in the manufacture of liquid crystal displays. During thermo-printing, defects of tag lines on LGPs may occur easily, and these defects are of two categories: bubbles and missing tag lines. These defects lack salient visual attributes, such as edge-based and region-based features, and as such, traditional methods fail to detect them. To address this, we propose a Dense-bilinear convolutional neural network (BCNN), an end-to-end defect detection network, utilizing Dense-blocks (Huang et al. , 2017), Bilinear feature layers (Lin et al. , 2015), and squeeze-and-excitation blocks (Hu et al. , 2018). Our network exploits fine-grained texture features, which leads to parameter reduction and accuracy enhancement. We validate our network on our LGP dataset containing 5,860 images from three cases: bubbles, tag line existence, and tag line missing. Our network outperforms AlexNet (Krizhevsky et al. , 2012), VGG (Simonyan and Zisserman, 2014) and ResNet (He et al. , 2016), on both the public and our LGP datasets with less GPU memory consumption.

Real-Time Defect Detection of Track Components: Considering Class Imbalance and Subtle Difference Between Classes

During the operation of the railway system, it is inevitable that there will be track defects that affect train operation safety, especially the defects of the rail and fastener. Therefore, it is necessary to detect these defects in time to ensure the safety of train operations. In recent years, with the development of deep learning and computer vision technology, intelligent detection of track defects has made great progress. However, the existing methods not only suffer from the scarcity of defective samples but also their fine-grained recognition is low due to subtle differences between similar classes; furthermore, their real-time performance also needs to be improved. To solve these problems, this article presents a real-time defect detection method for track components based on instance segmentation. First, an improved lightweight instance segmentation network is proposed for the segmentation and location of fastener and rail. Second, a method that detects fastener defects by analyzing the geometric features of the fastener masks is proposed, which can overcome the scarcity of defective fastener samples. A cascade defect detection network is proposed to realize multiresolution and high-resolution detection of rail, which improves the accuracy of rail defect detection. Finally, the TensorRT inference framework is used to accelerate the defect detection network and realize edge end deployment. The experimental results show that the proposed method can achieve 95.1% recall rate of fastener defects, 98% detection rate of rail defects, and 93.5% classification accuracy of rail defects; 33.4 FPS defect detection speed is implemented in the Jetson AGX Xavier embedded device, which verifies the accuracy and real-time performance of the proposed method.

Fabric Defect Segmentation Method Based on Deep Learning

Fabric defect detection plays an essential role in the textile production process, which was widely applied in the textile industry. For fabric defect detection, many algorithms have been proposed. However, lots of important problems, such as the accuracy of detection, the computational complexity of the algorithm, and data imbalance, still needed to be addressed for application in industrial production. In this article, we propose an efficient convolutional neural network for defect segmentation and detection. The design of this framework significantly alleviates the manual annotation cost of the data set; it only needs few defect samples combined with standard samples to learn the potential feature of defects and obtain the location of defects with high accuracy. The network is divided into two parts: segmentation and decision. First, the fabric data set without training is utilized as the input of the segmentation network. Then, the output of the segmentation network is applied as the raw materials for training the decision network. Finally, a well-trained network is used to obtain the location of defects with high accuracy. The proposed method only demands almost 50 defect samples to get accurate segmentation results and can achieve the requirement of real-time detection with a speed of 25 frames per second (FPS). The experimental results based on a public data set and three self-made fabric data sets show that the proposed method significantly outperforms eight state-of-the-art methods in terms of accuracy and robustness.

RCAG-Net: Residual Channelwise Attention Gate Network for Hot Spot Defect Detection of Photovoltaic Farms

The small hot spot defect detection for photovoltaic (PV) farms is a challenging problem due to the feature vanishing as the network deepens. To solve this challenging problem, a novel residual channelwise attention gate network (RCAG-Net) is proposed by employing a novel RCAG module to achieve multiscale feature fusion, complex background suppression, and defect feature highlighting. In RCAG-Net, the novel RCAG module first realizes feature fusion by adding the features of different scale layers. Next, global average pooling (GAP) and multilayer perceptron (MLP) are used to dimension reduction and refinement of the fused features, then yielding an attention map for channelwise feature reweighting by gate mechanism, which employs selective transmission of the convolution neural network (CNN)-extracted features to achieve informative feature filtering. Moreover, residual connection from the fused features to the final output facilitates the insertion of the new RCAG into some classical pretrained models, without breaking its initial behavior. Finally, the proposed approach is validated through a real defect detection system, and the experimental result clearly verifies its effectiveness for small hot spot detection of PV farms.

Combined Convolutional and LSTM Recurrent Neural Networks for Internal Defect Detection of Arc Magnets Under Strong Noises and Variable Object Types

This paper focuses on developing the acoustic-based detection of arc magnet internal defects under different noises and object types. The major challenge in such a detection case is that the traditional approaches mainly rely on expert knowledge and experiential features, which results in a poor generalization performance of the algorithm for new types of detection objects and is susceptible to noise interference. This work presents a novel detection framework based on an end-to-end one-dimensional (1D) convolutional long short-term memory (LSTM) model, where both the spatial and temporal features of the measured acoustic signals are extracted and then jointed for determining the internal defects of arc magnets. In addition, the LSTM layers are employed behind the 1D convolutional neural network, which makes the number of time steps in the LSTM layers for the temporal feature extraction is much smaller than the length of the input segments, thus the computational complexity of the LSTM layers can be highly reduced. Experimental results show that our method is superior to existed methods in the detection accuracy for the internal defects of arc magnets, and the diagnosis time per a single arc magnet is controlled at the millisecond, making it appropriate for real-time applications. Furthermore, the robustness of the proposed framework is validated through experiments on different signal-to-ratios and multiple object types of arc magnets.

Recurrent neural network NARX for distributed fault detection in wireless sensor networks

Recurrent neural network NARX for distributed fault detection in wireless sensor networks