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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Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults by Lijun Wang, Shengfei Ji, Nanyang Ji

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Winding Fault Detection in Power Transformers Based on Support Vector Machine and Discrete Wavelet Transform Approach by Bonginkosi A. Thango

“…This paper presents a novel diagnostic framework combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) classification to improve the detection of TWFs. …”
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Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection by Eleonora Iunusova, Andreas Archenti

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Assessing HMM and SVM for Condition-Based Monitoring and Fault Detection in HEV Electrical Machines by Riham Ginzarly, Nazih Moubayed, Ghaleb Hoblos, Hassan Kanj, Mouhammad Alakkoumi, Alaa Mawas

“…Hence, the aim of this paper is to present two data-based fault detection approaches, which are the support vector machine (SVM) and the Hidden Markov Model (HMM). …”
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Failure State Identification and Fault Diagnosis Method of Vibrating Screen Bolt Under Multiple Excitation of Combine Harvester by Jiaojiao Xu, Tiantian Jing, Meng Fang, Pengcheng Li, Zhong Tang

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Hybrid Deep Learning Approach for Accurate Detection and Multiclass Classification of Broken Conductor Faults in Power Distribution Systems by Firas Saadoon Mohammed Al-Jumaili, Mustafa Onat

“…It is shown that the proposed method has higher fault detection and classification accuracy compared to three traditional classification approaches, namely, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and three state-of-the-art methods: 1) Stockwell transform +SVM, 2) Fast Fourier Transform + SVM, and 3) Hilbert-Huang transform of vibration data and power spectral density + Artificial Neural Network. …”
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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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Detection of Broken Rotor Bars in Presence of Load Oscillations by Klemen Drobnic, Mitja Nemec, Henrik Lavric, Vanja Ambrozic, Rastko Fiser

“…If the detected spectral peak pair corresponds to a BRB, the fault indicator exceeds the threshold. …”
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Infrared Thermography-Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning by Usman Shafique, Syed Muhammad Alam, Umar Rashid, Wahab Javed, Haris Anwaar, Malik Shah Zeb, Talha Ahmad, Uzair Imtiaz, Frederic Nzanywayingoma

“…This paper addresses the critical issue of insulator fault detection in electric substations, emphasizing the importance of timely identification to prevent accidents. …”
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A Fault Identification Method for Electric Submersible Pumps Based on DAE-SVM by Peihao Yang, Jiarui Chen, Hairong Zhang, Sheng Li

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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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Self-Healing of Active Distribution Networks by Accurate Fault Detection, Classification, and Location by Sally El-Tawab, Hassan S. Mohamed, Amr Refky, A. M. Abdel-Aziz

“…The proposed algorithm utilized a discrete wavelet transform (DWT) to decompose the measured current and zero sequence current component of only one terminal (substation) to detect and classify all fault types with the identification of the faulted phase (s). …”
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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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Research on series arc fault detection method household loads based on voltage signals by Bin Li, Jiahui Shu, Feifan Cui

“…Compared with the detection results of various algorithms, it is verified that this method has more advantages in the identification of series arc fault. …”
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Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks by Atefeh Pour Shafei, J.Fernando A. Silva, J. Monteiro

“…The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. …”
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Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis by Mohammed Bouzidi, Abdelfatah Nasri, Omar Ouledali, Messaoud Hamouda

“…Research advocates for the integration of artificial neural networks with other machine learning methodologies, such as support vector machines, to improve fault prediction precision. …”
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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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