Cable Fault Diagnosis Research Articles

Cable fault diagnosis with generalization capability using incremental learning and deep convolutional neural network

Power cable faults may exhibit dynamic and differential changes attributable to variations in operating times and environmental conditions. However, the existing artificial intelligence (AI)-based fault diagnosis methods are lack of capability to handle this complexity of fault development. They can only diagnose given faulty types and will malfunction when dealing with newly-emerged faults, which accounts for the lack of generalization capability for AI-based methods. Given that, this paper proposes a cable fault diagnosis method with generalization capability utilizing incremental learning and deep convolutional neural network (DCNN). The idea of knowledge distillation is introduced to reserve the diagnosis information of the DCNN for original faults, and meanwhile, the DCNN is also modified by the classification loss of newly-emerged faulty samples, which equips the DCNN with generalization capability. The category features are obtained utilizing the refined DCNN, and sent to the constructed classifier for accurate incremental fault diagnosis. Experiments are conducted with field and simulated data. The obtained results validate the feasibility and effectiveness of the proposed method.

Robot dynamics-based cable fault diagnosis using stacked transformer encoder layers

Robot dynamics-based cable fault diagnosis using stacked transformer encoder layers

Optical Cable Fault Diagnosis and Auxiliary Decision-making Based on Knowledge Graph

The operational status of power communication optical cables is directly related to the safety of power transmission lines and communication systems. Traditional cable maintenance modes face challenges in fault localization. This article proposes a platform for optical cable fault diagnosis and decision support, which is constructed at three levels: the data layer, ontology layer, and application layer. The key aspect of this platform is the utilization of a knowledge graph to map the status of the cable system. The method proposed in this article effectively addresses the pain points of traditional maintenance modes.

Deep learning-based fault diagnosis and localization method for fiber optic cables in communication networks

Abstract With the arrival of the big data era and the development of new network technology, how to use big data technology to diagnose and locate fiber optic cable faults in communication networks has become a hot topic of current concern. Firstly, a combined generative adversarial network and convolutional neural network algorithm is proposed based on a deep learning algorithm, then an improved fault diagnosis model combining generative adversarial network and convolutional neural network algorithm is built, and finally, the combined generative adversarial network and convolutional neural network model is used to verify and analyze the fiber optic cable fault diagnosis. The results show that the accuracy of the DCGAN-CNN algorithm for fiber optic cable fault diagnosis is 98.5%, and the research results verify the effectiveness of the combined generative adversarial network and convolutional neural network model for fiber optic cable fault diagnosis. This study can accurately and comprehensively solve the problem of fiber optic cable faults in communication networks and thus play a guiding reference value for developing fault diagnosis in Chinese communication networks.

Multiscale Analysis of the Aging Process of Cable Insulation

Cables, transmitting both power and signal, are essential in electric power systems. Due to the combined effect of electromagnetic and thermal fields, cable insulation aging starts from the destruction of polymer molecular structure and deteriorates in the form of insulation defects, resulting in the degradation of cables eventually. In this article, multiscale simulation is implemented to comprehensively analyze the insulation aging mechanism and reveal the variation law of aging characteristics. Regarding multiscales, by molecular dynamics (MD) simulation and finite-element method (FEM), the material and electrical parameters are calculated and quantitatively analyzed. According to the findings, tiny molecular products resulting from thermal and electrical aging are the main causes of dielectric constant and electrical conductivity variations. Field distribution inside the cable is distorted due to the degradation of material parameters, thus changing power loss and electrical parameters. The multiscale analysis method in this article can make the cable insulation aging analysis more time- and resource-efficient, providing the theoretical basis and data support for cable fault diagnosis and remaining useful life prediction.

The Cable Fault Diagnosis for XLPE Cable Based on 1DCNNs-BiLSTM Network

Diagnosing the fault type accurately from a variety of faults is very essential to ensure a stable electricity supply when a short-circuit fault occurs. In this paper, a hybrid classification model combining the one-dimensional convolutional neural network (1D-CNN) and the bidirectional long short-term memory network (BiLSTM) is proposed for the classification of cable short-circuit faults to improve the accuracy of fault diagnosis. Sample sets of the current signal for single-phase grounding short circuit, two-phase grounding short circuit, two-phase to phase short circuit, and three-phase grounding short-circuit are obtained by the simulink model, and the signal is input to this network model. The local features of the cable fault signals are extracted using 1D-CNN and the fault signal timing information is captured using BiLSTM, which enables the diagnosis of cable faults based on the automatically extracted features. The experimental results of the simulation show that the model can obtain a good recognition performance and can achieve an overall accuracy of 99.45% in classifying the four short-circuit faults with 500 iterations. In addition, the analysis of loss function curves and accuracy curves shows that the method performs better than networks with only temporal feature extraction, such as 1D-CNN and LSTM.

Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks

To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time-frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time-frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time-frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre-training and supervised inverse fine-tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN-based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.

Research on fault diagnosis method of aviation cable based on improved Adaboost

In order to solve the problems of short circuit, open circuit and insulation faults in aviation cables, a fault diagnosis method based on BP-Adaboost algorithm is proposed in this paper. The BP neural network is used as the weak classifier in the Adaboost algorithm, and many weak classifiers are composed a strong classifier with stronger classification performance to diagnose fault categories. The BP-Adaboost fault diagnosis model is established, and the BP-Adaboost algorithm is improved to adapt to the multi-classification faults of cables, so as to identify the short circuit, open circuit, and insulation faults in the aircraft cable as well as normal working conditions. The accuracy of classification is analyzed; the results of the algorithm are analyzed by Matlab software, and the analysis results show that the improved BP-Adaboost algorithm has a relatively good classification performance for multi-class aviation cable fault diagnosis. Finally, the feasibility of the algorithm proposed is verified through an example combined with cable fault detection equipment.

Research on Key Technologies of High Voltage Power Cables in Smart Grid

The paper discusses important issues such as high-performance materials, electrical tree aging, space charge, insulation structure and technology, and cable operation detection in cable research and development. It is believed that DC cables will be used in power transmission for a long time in the future. It will receive more and more attention, but it is also facing the problems of insulation reliability and the development of new materials due to space charge and nonlinear temperature characteristics. The paper analyses the main reasons for the failure of power cables in the smart grid, and proposes several advanced power cable fault diagnosis technologies, and studies their principles and applications in order to improve the efficiency of fault diagnosis of the smart grid and protect the power grid. Operational reliability.

Analysis of Power Cable Fault Diagnosis and Electric Field Detection Technology Based on Computer Control System

With the continuous improvement of China’s power system requirements, a good and stable power supply is particularly important. Among them, the operation of power cable equipment is closely related to the quality of power system. Therefore, continuous improvement and reform of power cable equipment fault analysis and electric field detection technology can improve the quality and timeliness of power system. Based on the analysis of power cable fault and electric field detection technology by computer control system, this paper summarizes the related fault problems and electric field detection technology, so as to improve the quality of China’s power system.

Non-intrusive Cable Fault Diagnosis Based on Inductive Directional Coupling

This paper develops and applies an inductive directional coupling technology based on spread spectrum time domain reflectometry for non-intrusive power cable fault diagnosis. Different from existing capacitive coupling approaches with large signal attenuation, an inductive coupling approach with a capacitive trapper is proposed to reduce signal attenuation, restrict transmitting the detection signal to power source, and eliminate the effect of the power source impedance mismatch. Moreover, a novel structure with two parallel couplers is developed to enhance the effect of capacitive trapper with small capacitance. The development, design, analysis, and implementation of the proposed approach are discussed in details. A series of simulations and experiments on cables with different fault modes at different locations are conducted, along with comparison with existing capacitive coupling approaches, to verify and demonstrate the effectiveness of the proposed method.

Non-Intrusive Cable Fault Diagnosis Based on Inductive Directional Coupling.

This paper presents and applies an inductive directional coupling technology based on spread spectrum time domain reflectometry (SSTDR) for non-intrusive power cable fault diagnosis. Different from existing capacitive coupling approaches with large signal attenuation, an inductive coupling approach with a capacitive trapper is proposed to restrict the detection signal from transmitting to power source and to eliminate the effect of the power source impedance mismatch. The development, analysis, and implementation of the proposed approach are discussed in detail. A series of simulations and experiments on cables with different fault modes are conducted, along with comparison of existing capacitive coupling, to verify and demonstrate the effectiveness of the proposed method.

A cable fault recognition method based on a deep belief network

To meet the requirement of online diagnosis of a cable fault, certain problems should be addressed. Therefore, in this paper, we propose an online cable fault diagnosis method. First, we establish a simulation model of an underground cable distribution system for collecting fault signals. Second, a deep belief network (DBN) is created by the deep learning theory for identifying a cable fault. Finally, we extract the characteristics of the fault signal and classify them into a large number of fault data automatically. A comparison of the results of the cable fault recognition with the proposed method and conventional shallow neural network shows that the DBN is of 97.8%, the conventional back propagation (BP) network is of 86.6%, ACCLN is of 94.1%, which demonstrate that the DBN-based cable fault recognition method has distinct advantages compared with a shallow neural network.

De-embedding Unmatched Connectors for Electric Cable Fault Diagnosis

In order to make accurate reflectometry measurements on electric cables for fault diagnosis, connector de-embedding is a procedure for compensating measurement distortions caused by unmatched connectors. The key step in such a procedure is the characterization of the connectors, which is realized through measurements on a pair of connectors linked by a short cable segment. The analysis for deducing the characteristics of a single connector from measurements made on an assembled pair is known as the bisection problem. In this paper, after recalling the underdetermined nature of the bisection problem, a practically effective de-embedding procedure is proposed based on a particular regularization technique. Numerical examples are presented to illustrate the effectiveness of this procedure.

Design of Aircraft Cable Fault Diagnose and Location System Based on Aircraft Airworthiness Requirement

Aircraft cable have the special status in aviation domain, with the improvement of aircraft performance requirements, the amount of the single wire is also increasing gradually. Demanding of its performance and maintaining is increasingly day by day. An aircraft cable fault diagnosis and location system based on Phase Detection Frequency Domain Reflectometry (PDFDR) is presented in this paper, which is used to diagnose and locate the special aircraft cable faults, such as intermittent fault, continuous fault and cross-connect fault. With the threshold denoising method on Hilbert Huang Transform, the studied approach is developed to detect and locate aircraft cable “hard defects” (i.e. opens and shorts) based on Phase Detection Frequency Domain Reflectometry (PDFDR). By analyses the differences of amplitude and phase in superimposed signal which from the incident signal and the reflected signal on defects, the shortage of Phase-Detection Frequency Domain Reflectometry (PDFDR) simply using in locating aircraft cable defects is solved. Utilizing the strong calculating capability and the abundant function library of MATLAB, the signal processing turn to be easily realized, moreover LabVIEW help the system to be more reliable and updated easily.