Cable joint fault detection for the ring main unit based on an adaptive TNPE algorithm

Tan, Lei; Li, Peng; Tao, Fei; Miao, Aimin; Cao, Min

doi:10.1002/widm.1336

Cited by 6 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, the DAEbased LCI model can monitor the vehicle driving behaviour online and collect new samples in real time. Then, the online model is obtained by updating the identified historical model with new samples according to the approximate linear dependence (ALD) [44] condition. The proposed integrated decision framework includes the following three steps:…”

Section: Vehicle Lci and Lcd Algorithm A Algorithm Frameworkmentioning

confidence: 99%

A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

Han

2020

IEEE Access

View full text Add to dashboard Cite

Lane-changing (LC) is a critical task for autonomous driving, especially in complex dynamic environments. Numerous automatic LC algorithms have been proposed. This topic, however, has not been sufficiently addressed in existing on-road manoeuvre decision methods. Therefore, this paper presents a novel LC decision (LCD) model that gives autonomous vehicles the ability to make human-like decisions. This method combines a deep autoencoder (DAE) network with the XGBoost algorithm. First, a DAE is utilized to build a robust multivariate reconstruction model using time series data from multiple sensors; then, the reconstruction error of the DAE trained with normal data is analysed for LC identification (LCI) and training data extraction. Then, to address the multi-parametric and nonlinear problem of the autonomous LC decision-making process, an XGBoost algorithm with Bayesian parameter optimization is adopted. Meanwhile, to fully train our learning model with large-scale datasets, we proposed an online training strategy that updates the model parameters with data batches. The experimental results illustrate that the DAE-based LCI model is able to accurately identify the LC behaviour of vehicles. Furthermore, with the same input features, the proposed XGBoost-based LCD model achieves better performance than other popular approaches. Moreover, a simulation experiment is performed to verify the effectiveness of the decision model. INDEX TERMS Autonomous vehicle, lane-changing identification, lane-changing decision-making, deep autoencoder network, XGBoost.

show abstract

Section: Vehicle Lci and Lcd Algorithm A Algorithm Frameworkmentioning

confidence: 99%

A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

Han

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

Application of machine learning in wire damage detection for safety procedure

et al. 2022

View full text Add to dashboard Cite

Cable current-carrying status analysis and early fault warning method based on temperature information

Yang

Zhang

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Long-term operation of cables and equipment under abnormal currents can cause insulation aging and reduce the service life of the equipment. Identifying the current-carrying status accurately and early detection of potential failures of power cables is particularly significant for ensuring the operation of power equipment safely and economically. This paper proposed a method for detecting the current-carrying status of power cables and an early fault warning based on temperature information. Based on the theoretical basis of the relationship between temperature and current. According to IEC 61000-3-7 and IEC 60038 standards, the current-carrying state evaluation method is analyzed and given, and the early fault warning model is established. Finally, the paper takes the cable connector and the lightning arrester in the power ring network cabinet as an example to verify the method's feasibility. It shows that the characteristic information of the relative temperature variation curves under different current carrying states significantly differs. Includes the slope of hour 9, the slope of hour 2, and the maximum. Then the total duration under abnormal current operation is calculated, and the early warning of faults can realize.

show abstract

Cable joint fault detection for the ring main unit based on an adaptive TNPE algorithm

Cited by 6 publications

References 22 publications

A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

Application of machine learning in wire damage detection for safety procedure

Cable current-carrying status analysis and early fault warning method based on temperature information

Contact Info

Product

Resources

About