Kronecker convolutional feature pyramid for fault diagnosis in rolling bearings by Sadia Batool, Abbas Ali Abid, Muhammad Asif, Sagheer Abbas, Khan Muhammad Adnan, Asghar Ali Shah, Taher M. Ghazal

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Early Detection of ITSC Faults in PMSMs Using Transformer Model and Transient Time-Frequency Features by Ádám Zsuga, Adrienn Dineva

“…To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. …”
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Feature engineering for fault detection and diagnosis in Power Transmission Lines using a tree-based approach by Hassan N. Noura, Zaid Allal, Ola Salman, Khaled Chahine

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Flexible Adaptive Marine Predator Algorithm and Its Application in Fault Detection for Wind Turbines by WANG Wen, YI Jiabiao

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Improved railway track faults detection using Mel-frequency cepstral coefficient and constant-Q transform features by Rahman Shafique, Khadija Kanwal, Venkata Chunduri, Gyu Sang Choi, Imran Ashraf

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Advanced bearing fault detection at varying rotational speeds using PSO-optimized SVM and CDET feature selection by Hongxu Chai, Xiaoshi Ma, Feng Zhu, Yandong Hu

“…Extensive experiments on benchmark databases demonstrate that the recommended tactic surpasses conventional tactics regarding fault detection accuracy, stability, and feature compactness. …”
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Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…This paper concludes with a review of the progress in fault identification in ICE components and prospects, highlighted by an experimental investigation using 16 machine learning algorithms with seven feature selection techniques under three load conditions to detect faults in a four-cylinder ICE. …”
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Detection of Stator Faults in Three-Phase Induction Motors Using Stray Flux and Machine Learning by Ailton O. Louzada, Wesley A. Souza, Avyner L. O. Vitor, Marcelo F. Castoldi, Alessandro Goedtel

“…Traditional fault detection methods rely on stator current or vibration analysis, each with limitations regarding sensitivity to specific failure modes and dependence on motor power ratings. …”
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Research on Arc Fault Detection Based on Conditional Batch Normalization Convolutional Neural Network with Cost-Sensitive Multi-Feature Extraction by Xin Ning, Tianli Ding, Hongwei Zhu

“…Therefore, the timely detection of arc faults and implementation of circuit-breaking measures are crucial for ensuring safety, preventing fires, and maintaining the stable operation of power systems. …”
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Fault detection algorithm for underground conveyor belt deviation based on improved RT-DETR by AN Longhui, WANG Manli, ZHANG Changsen

“…Current research on conveyor belt deviation detection mainly focuses on extracting the straight-line features of belt edges. …”
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Fault detection in electrical power systems using attention-GRU-based fault classifier (AGFC-Net) by Deepen Khandelwal, Prateek Anand, Mayukh Ray, Sangeetha R. G.

“…Conventional fault detection technologies tend to possess low accuracy rates, weak feature extraction, as well as limitations in generalizability across variegated faults. …”
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ROCK FRACTURES NEAR FAULTS: SPECIFIC FEATURES OF STRUCTURAL‐PARAGENETIC ANALYSIS by Yu. P. Burzunova

“…The new approach to structural‐paragenetic analysis of near‐fault fractures [Seminsky, 2014, 2015] and specific features of its application are discussed. …”
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Application of Machine Learning Techniques for Bearing Fault Diagnosis by Sarra Eddai, Nabih Feki, Ahmed Ghorbel, Abdelkhalak El Hami, Mohamed Haddar

“…Notably, the SVM algorithm demonstrates exceptional performance, attaining a 99.2% accuracy rate in inner-race fault identification. This investigation provides a comprehensive analysis of the Case Western Reserve University (CWRU) dataset, data preprocessing procedures, feature extraction techniques, and machine learning algorithms utilized for fault detection. …”
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A hybrid machine learning and ied-based fault detection scheme for microgrids by Hamid Radmanesh, Abolfazl Hadadi

“…This paper proposes a new intelligent fault detection approach that leverages advanced signal processing techniques, including modified Variable Mode Decomposition (MVMD) for feature extraction, combined with a hybrid machine learning (ML) model. …”
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A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder by Hoyeon Lee, Jaehong Yu

“…Finally, we construct the fault detection model by applying the one-class classification algorithm to the latent feature vectors of training signals. …”
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Yaw Noise Fault Detection for Wind Turbines Based on ResNet-Transformer Model by CHEN Yanan, HU Kaikai, LI Ziyuan, WANG Lipeng

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Multi-Scale Residual Convolutional Neural Network with Hybrid Attention for Bearing Fault Detection by Yanping Zhu, Wenlong Chen, Sen Yan, Jianqiang Zhang, Chenyang Zhu, Fang Wang, Qi Chen

“…This paper proposes an advanced deep convolutional neural network model for motor bearing fault detection that was designed to overcome the limitations of traditional models in feature extraction, accuracy, and generalization under complex operating conditions. …”
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Research on series arc fault detection method household loads based on voltage signals by Bin Li, Jiahui Shu, Feifan Cui

“…Abstract In order to accurately detect series arc fault, this paper proposes a series arc fault detection method based on voltage signal which introduces inception with multi-scale parallel convolution operation, and combines bidirectional long short-term memory recurrent network (BiLSTM) with attention mechanism. …”
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Intelligent UAV health monitoring: Detecting propeller and structural faults with MEMS-based vibration by Temel Sonmezocak

“…These models are designed to perform fault detection both before and during flight based on the vibration data of a multirotor UAV. …”
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Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain by Jesus Adolfo Cariño-Corrales, Juan Jose Saucedo-Dorantes, Daniel Zurita-Millán, Miguel Delgado-Prieto, Juan Antonio Ortega-Redondo, Roque Alfredo Osornio-Rios, Rene de Jesus Romero-Troncoso

“…The proposed methodology is validated experimentally by monitoring the vibration measurements of a kinematic chain driven by an induction motor. Two faults are induced in the motor to validate the method performance to detect anomalies and adapt the feature reduction and novelty detection modules to the new information. …”
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