Fault Location Method for Communication Link in Smart Substation Based on Deep Learning by Zhiyong PI, Yi ZHU, Xuan LIAO, Zhenxing LI, Hao FANG, Pei WU

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Analysis of seismicity in the Haicheng-Xiuyan region based on dense array data and deep learning methodsKey points by Zemin Liu, Weitao Wang, Lu Li, Zihao Li, Ziye Yu, Songyong Yuan, Lanshu Bai

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Multiple-Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA by Min Zhang, Zhenyu Cai, Wenming Cheng

“…The results of a case study of the rolling bearings faults data from Case Western Reserve University show that (1) the proposed intelligent method (MFE_PPA_MSVM) improves the classification recognition rate; (2) the accuracy will decline when the number of fault patterns increases; (3) prediction accuracy can be the best when the training set size is increased to 70% of the total sample set. …”
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RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT) by CAO YaLei, DU YingJun, WEI Guang, DONG XinMin, GAO LiPeng, LIU YuXi

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A Survey of Broken Rotor Bar Fault Diagnostic Methods of Induction Motor by Asad Bilal, Vaimann Toomas, Rassõlkin Anton, Kallaste Ants, Belahcen Anouar

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Rapid Fault Diagnosis Method of Elevator System Based on Multiattribute Decision Making by Dapeng Niu, Chenshu Qi, Guanghai Li, Hongru Li, Hali Pang

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An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis by Jian-Hua Zhong, JieJunYi Liang, Zhi-Xin Yang, Pak Kin Wong, Xian-Bo Wang

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Review of Fault Detection and Diagnosis Methods in Power Plants: Algorithms, Architectures, and Trends by Camelia Adela Maican, Cristina Floriana Pană, Daniela Maria Pătrașcu-Pană, Virginia Maria Rădulescu

“…However, only 30.3% of the articles addressed the full diagnostic pipeline and merely 17.3% targeted system-level faults. …”
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A Novel Rail Damage Fault Detection Method for High-Speed Railway by Yu Wang, Bingrong Miao, Ying Zhang, Zhong Huang, Songyuan Xu

“…A brand-new type of speedy rail inspection robot and its fault detection method are proposed to solve a number of problems, such as the difficulty and low accuracy of real-time online detection of rail defects and damage in speedy railways. …”
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Fault Diagnosis of Rolling Bearing Based on Modified Deep Metric Learning Method by Zengbing Xu, Xiaojuan Li, Hui Lin, Zhigang Wang, Tao Peng

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Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger by Xiaogang Qin, Shiwei Yan, Haibo Xu, Yi Gao, Yanbing Yu, Jinjiang Wang

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A Quantum Q-Learning Fault Diagnosis Method for Intelligent Manufacturing Equipment by Yi Chen, Kai Deng, Xuelin Du, Zichao Chang, Tong Wan

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Data-driven fault identification method of RV reducer used in industrial robot by Dongdong Guo, Yan Zhang, Xiangqun Chen, Hao Peng, Zongrui Jiang, Haitao Ma, Wenbo Du

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Fault location method for multi-section hybrid lines considers velocity uncertainty by Liwei Xie, Youpeng Wang, Yang Xiao, Xiangjun Zeng, Yan Mo, Zhuo Long

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Application of different edge detection methods for faults identification in Anza Basin, Kenya by Gege Li, Qing Chen, Bingqiang Yuan, Xianfeng Tan, Hao Chen, Xiaoyu Huang, Lang Pan

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Acoustic characterization of a three-phase asynchronous machine under stator unbalance defects by Abderrahman El Idrissi, Aziz Derouich, Said Mahfoud, Najib El Ouanjli, Ahmed Chantoufi, Youness El Mourabit

“…Diagnosing faults in three-phase asynchronous machines (ASMs) is crucial in industrial environments, where non-invasive techniques such as acoustic analysis and thermography are preferred for detecting malfunctions in these machines. …”
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Application Study of Distributed Grounding Line Selection Method Based on GOOSE Communication by Hongshan CHEN, Jiang YU, Miao JIANG, Yong SHI, Wei HOU

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Calculation Method and Characteristic Analysis for Fault Current of Permanent Magnet Direct-Drive Wind Power System considering Positive and Negative Sequence Decomposition by Botong Li, Qing Zhong, Weijie Wen, Bin Li, Xiaolong Chen

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Fault Diagnosis of Industrial Process Based on FDKICA-PCA by ZHANG Jing, ZHU Fei-fei, LIU Jia-xing, WANG Jiang-tao

“…Because the dynamic characteristics of autocorrelation and lag correlation in production process are neglected in fault diagnosis,Kernel Independent Component AnalysisPrincipal Component Analysis (KICAPCA) is very poor in detecting small and gradual faults because of lacking available variable contribution analysis.In this paper, a dynamic kernel independent component analysis (KICAPCA) fault diagnosis method based on wavelet packet filtering is proposed.This method integrates wavelet packet filtering theory and AR model prediction data characteristics into KICAPCA to extract the feature information of process variable autocorrelation and lagrelated .In this paper, KICAPCA algorithm is used to extract the independent components and principal components of process variables to determine the control limits of three monitoring indicators T2, SPE,I2.Nonlinear contribution graph is used for fault diagnosis, and the advantage of FDKICAPCA method is verified by simulation results of Tennessee process.…”
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Spectral Correlation Demodulation Analysis for Fault Diagnosis of Planetary Gearboxes by Xiaohui Duan, Rongzhou Lin, Zhipeng Feng

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