An intelligent protection scheme based on support vector machine for fault detection in microgrid using transient signals in protection scheme by Tiwari Shankarshan Prasad

“…This paper presents a protection scheme based on support vector machines to detect faults under such tedious conditions. …”
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Software Defects Identification: Results Using Machine Learning and Explainable Artificial Intelligence Techniques by Momotaz Begum, Mehedi Hasan Shuvo, Imran Ashraf, Abdullah Al Mamun, Jia Uddin, Md Abdus Samad

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Application of KTA-KELM in Fault Diagnosis of Rolling Bearing by Zhuo Wang, Wenjun Zhao, Tao Ma, Zhijun Li, Bo Qin

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UHVDC Transmission Line Fault Identification Method Based on Generalized Regression Neural Network by XIE Jia, LIU Feng, KE Yanguo, YIN Zhen, RUAN Wei, YAO Jinming

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CEEMDAN-Based Permutation Entropy: A Suitable Feature for the Fault Identification of Spiral-Bevel Gears by Lingli Jiang, Hongchuang Tan, Xuejun Li, Liman Chen, Dalian Yang

“…In order to assess the sensibility of the permutation entropy features, the support vector machine (SVM) is used as the classifier for fault mode identification, and the diagnostic accuracy can verify its sensibility. …”
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Fault Diagnosis Method for Rolling Bearing based on Least Squares Mapping and SVM by Zhao Yu, Li Ke, Su Lei, Chen Peng

“…Aiming at the problem of extraction difficulty of early non-stationary weak fault signal feature,low resolution of characteristic parameter,early fault diagnosis difficult exist in the rolling bearing fault diagnosis,a fault diagnosis method based on least squares mapping（LSM） fault characteristic parameter optimization and support vector machine（SVM） is proposed.Firstly,the non-dimensional symptom parameters（NSPs） in the time domain can reflect the features of the vibration signals measured in each state are calculated.Then,the high sensitivity symptom parameters are built by optimizing the calculated non-dimensional symptom parameters（NSPs） in the time domain with the LSM theory.Finally,the symptom parameters selecting by sensitivity identification factor are input to the SVM for the fault diagnosis,the fault type of bearing is identified through the sequential inference diagnosis.The practical examples of fault diagnosis for a motor bearing are shown to verify that the method is effective.…”
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Fault Line Selection of Single Phase Grounding Based on Wavelet Packet Full Frequency Analysis and OS-ELM by JI Wen-lu, ZHAO Xiao-long, ZHANG Ming, YANG Hong-lei, WENG Jia-ming

“…In order to enhance effectiveness of single-phase ground fault feature extraction and to achieve exactly identification of fault line selection，a new fault line selection method of single phase grounding fault based on wavelet packet and online sequential extreme learning machine ( OS-ELM) is proposed. …”
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Reconstruction of linear block code parity-check matrix based on fault-tolerant Gaussian elimination by WANG Zhongyong, ZHANG Hong, GONG Kexian, WANG Wei, ZHANG Ling, ZHU Junhao

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Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery by Wenlong Fu, Jiawen Tan, Xiaoyuan Zhang, Tie Chen, Kai Wang

“…Then the multivariate autoregressive (MAR) model of all IMFs was established, whose order was determined by Schwartz Bayes Criterion (SBC), and all parameters of the model were identified blindly through QR decomposition, where key features were subsequently extracted via principal component analysis (PCA) to construct feature vectors of different fault types. Afterwards, a hybrid optimization algorithm combining mutation operator, grey wolf optimizer (GWO), and sine cosine algorithm (SCA), termed mutation hybrid GWO-SCA (MHGWOSCA), was proposed for parameter selection of support vector machine (SVM). …”
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Gearbox Fault Diagnosis Method Based on Improved Multi-scale Mean Permutation Entropy and Parameter Optimization SVM by Guo Panpan, Zhang Wenbin, Cui Ben, Xu Han

“…Therefore, this study proposes a gearbox fault identification method based on the improved multi-scale mean permutation entropy and the parameter optimization support vector machine（SVM）. …”
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Feature extraction and fault diagnosis of gearbox based on ICEEMDAN, MPE, RF and SVM by DING Xiaofeng, ZHANG Yuhua

“…To solve the challenges related to non-stationary vibration signals in gearboxes, i.e. difficult feature extraction, high redundancy of feature vectors and low fault identification rate, this paper proposed a method of feature extraction and fault diagnosis of gearboxes based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multi-scale permutation entropy (MPE), random forest (RF) feature importance ranking and support vector machine (SVM). …”
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Title not available by Bouali, Fakhreddine, Fedala, Semchedine, André, Hugo, Felkaoui, Ahmed

“…Finally, the classification is carried out by a cubic support vector machine (SVM) for the detection and identification stages of various bearings fault conditions. …”
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Advanced Non-Unit Protection Strategy for MMC-HVDC Grids Leveraging Two-Dimensional High-Frequency Characteristics via HHT and SVM by Chenglin Ren, Yige Hu, Xiang Lu, Yue Chen, Kepeng Xia, Hailin Li

“…The rapid development of direct current (DC) grids poses significant challenges to the speed, reliability, and selectivity of fault protection systems. These systems are required to identify and distinguish between internal and external faults despite the constraints of limited information and time. …”
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Bearing Fault Prediction Based on Mixed Domain Features and GWO-SVM by Xuan Zhou, Ruiyang Xia, Zhaodong Zhang, Sasa Duan, Mao Cheng, Chengjiang Zhou, Min Mao

“…We propose a bearing fault identification algorithm based on grey wolf optimizer (GWO) to address the common problems of high signal noise, inability of a single indicator to accurately reflect the true state of bearings, and optimization of support vector machine (SVM) prediction model parameters in bearing fault identification. …”
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A study on rolling bearing fault diagnosis using RIME-VMD by Zhenrong Ma, Ying Zhang

“…Finally, the sample entropy of the reconstructed signal is calculated as a fault feature and input into a Support Vector Machine (SVM) for rapid identification and diagnosis of various rolling bearing fault types. …”
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Fault location and isolation technology for power grid automation based on intelligent algorithms by Qi Guo, Fuhe Wang, Suxia Cheng, Ke Wang, Yifan Zhang

“…The FIA algorithm then uses the FLA output to evaluate fault severity and select the best fault isolation strategy. …”
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Bearing fault diagnosis for high-speed train based on improved VMD and APSO-SVM by ZHANG Qingsong, ZHANG Bing, QIN Yi

“…The fault features obtained by VMD were input into SVM for different bearing fault identification. …”
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SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces by M. P. Michalak, C. Gerhards, P. Menzel

“…<p>The study presents a novel approach for fault detection on subsurface geological homoclinal interfaces (slopes) using a supervised learning algorithm and careful input variable (feature) selection. …”
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Early Fault Detection in Electro-Pneumatic Actuators using Mathematical Modelling and Machine Learning: A Bottling Company Case Study by Samuel Olufemi Amudipe, Adeyinka Moses Adeoye, Aderonke Oluwaseunfunmi Akinwumi, Rotimi Adedayo Ibikunle, Segun Adebayo

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SEISMIC BELTS AND ZONES OF THE EARTH: FORMALIZATION OF NOTIONS, POSITIONS IN THE LITHOSPHERE, AND STRUCTURAL CONTROL by Semen I. Sherman, Olga G. Zlogodukhova

“…The research data in the present publication confirm strong arguments in favor of transition to quantitative classification of SZs, identification of faults which are active in real time and function as concentrators of earthquake foci, and evaluation of parameters of fault zones which determine space­and­time locations of earthquake foci.This publication demonstrates the need to develop tectonophysical models of SPs and apply such models to gain a more comprehensive understanding of interactions/correlations between seismic zones in cases of catastrophic earthquakes and/or closely spaced SBs with similar states of stresses.…”
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