Fault early warning is a challenge in the field of operation and maintenance. Considering the improvement of accuracy and real-time standards, as well as the explosive growth of operation and maintenance data, traditional manual experience and static threshold can no longer meet the production requirements. This research fully digs into the difficulties in fault early warning and provides targeted solutions in several aspects, such as difficulty in feature extraction, insufficient prediction accuracy, and difficulty in determining alarm threshold. The TCAG model proposed in this paper creatively combines the spatiotemporal characteristics and topological characteristics of specific time series data to apply to time series prediction and gives the recommended dynamic threshold interval for fault early warning according to the prediction value. A data comparison experiment of a core router of Ningxia Electric Power Co., Ltd. shows that the combination of topological data analysis (TDA) and convolutional neural network (CNN) enables the TCAG model to obtain superior feature extraction capability, and the support of the attention mechanism improves the prediction accuracy of the TCAG model compared to the benchmark models.

https://www.mdpi.com/2076-3417/12/20/10502/pdf?version=1666094857

A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU

Real-time monitoring of the power cable state has tremendous significance for ensuring the safe and economic operation of mine power distribution systems. However, due to the harsh conditions of underground coal mines, it is difficult for the cable monitoring system operating in underground coal mines to carry out large-scale calculations to diagnose the grounding fault of the cable. Additionally, there are many types of cable grounding faults, such as single grounding fault and two-phase ground fault. Therefore, how to determine the type of grounding fault quickly through effective calculations and alarms to select the grounding cable is always a difficult task. In this study, to reduce the complexity of cable insulation state classification, we develop a novel classification method based on the decision tree algorithm. Concerning the zero-sequence network under different insulation conditions, the first calculation’s positive and negative values were generated to identify whether the cable insulation was symmetrical. Then, the insulation degradation phase is identified by the relationship between the three-phase voltage phase angle and the current difference variation between the beginning and end of the line. By substituting the correlation quantities collected by a wide-area synchronous measurement system into different equations, the whole grid’s decision tree was constructed in different insulation states. Then, the insulation state of each line was evaluated according to the conductance value. The effectiveness of the proposed method was verified using a 35/6 kV mine power distribution system model based on the MATLAB/Simulink platform. The test results illustrate that the method can accurately diagnose the power cable insulation state based on the decision tree of whether the grid three-phase loads operate in an ungrounded mode or not.

https://www.mdpi.com/2227-9717/11/3/795/pdf?version=1678262624

Decision-Making Method for Mine Cable Insulation Monitoring and Grounding Fault Diagnosis

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Recent advancements in machine learning have significantly enhanced early detection and treatment strategies for CVDs. While electrocardiogram (ECG) signals are commonly used for detection, additional signals like arterial blood pressure (ABP) and central venous pressure (CVP) provide a comprehensive view of the cardiovascular system. However, acquiring such extensive datasets is challenging due to resource constraints, privacy issues, and ethical considerations. This paper introduces a novel Multichannel Conditional Wasserstein Generative Adversarial Network (MC-WGAN) capable of simultaneously generating synthetic ECG, ABP, and CVP signals. The MC-WGAN model addresses the data scarcity issue by providing high-fidelity synthetic data that mirrors real physiological signals, facilitating better simulation, diagnosis, and treatment planning. Evaluation against the MIT-BIH Arrhythmia Database demonstrated the model’s strong performance, with competitive metrics such as RMSE, PRD, and FD, particularly excelling in the generation of ECG and ABP signals. MC-WGAN surpasses other generative models by simultaneously replicating multiple physiological signals, offering a comprehensive view of cardiovascular health. This advancement enhances diagnostic accuracy and risk stratification, setting a new standard in synthetic biomedical signal generation, and paving the way for more personalized and effective clinical interventions.

Synthesis of Multimodal Cardiological Signals using a Conditional Wasserstein Generative Adversarial Network

Detecting faults in communication wiring systems is always an intriguing research topic. Most current fault finding approaches are based on reflectometry. However, traditional reflectometry requires prior knowledge, such as the cable propagation wave velocity, to analyze the cable state and cannot determine the severity of cable faults. This paper introduces a fault severity prediction method based on a one-dimensional convolution residual network without cable prior knowledge. By correlating the channel transfer function (CTF) with the fault information, a pilot-based deep learning network that can extract fault features more accurately emerges. The method was applied to a communication cable fault detection experiment on a software-defined radio (SDR) platform to verify its effectiveness. Compared with other traditional detection methods, the proposed method is simpler and has better performance.

DeepFaultNet: Predicting Fault Severity of Communication Cable With Hybrid-ResCNN

Electrocardiography(ECG) is a non-invasive tool used to identify the cardiovascular diseases. ECG classification studies have been concerned and made progress well. However, the problems about categories imbalance and absence of labelled clinic data are still dramatically hindered research development. Recently, generative models have been verified as a possible way to handle the data scarcity issues. For ECG synthesis, to the best of our knowledge as the reason of time sequences and multiple labels constraints, no model can generate ECG corresponding to clinic data.In this paper, we present a novel multi-label conditional generative adversarial network, named MLCGAN. To synthesise reasonable long-term multi-lead data, multi-label mixing module is devised to combine with our improved WaveGAN. Moreover, the sampling strategy based on multilabels distribution is proposed. Comprehensive experiments demonstrate that MLCGAN can generate ECG data satisfied the clinic diagnose requirement and improve the performance of RestNet based ECG classifier. 

MLCGAN: Multi-Lead ECG Synthesis with Multi Label Conditional Generative Adversarial Network

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DeepFaultNet: Predicting Fault Severity of Communication Cable With Hybrid-ResCNN

MLCGAN: Multi-Lead ECG Synthesis with Multi Label Conditional Generative Adversarial Network