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Sep 15, 2024 ， This study presents a novel fault diagnosis method using vibration data, MM-1D-LBP feature extraction, and a hybrid LSTM-1D-CNN network. High success rates ...
2 days ago ， This study proposes an improved few-shot learning model of the Siamese network residual Visual Geometry Group (VGG). This model combined with time–frequency ...
Sep 9, 2024 ， This study proposes a bearing fault diagnosis method based on a symmetric two-stream convolutional neural network (CNN).
Sep 23, 2024 ， This paper presents the application of the transfer learning of a convolutional neural network to the fault diagnosis of permanent magnet synchronous motors.
2 days ago ， This study proposes a novel feature extraction method that leverages multi-strategy optimization algorithms to enhance the accuracy and efficiency of rotating ...
4 days ago ， An opti- mization algorithm was used in this study to obtain the CNN hyperparameters that maximized the model's robustness and accuracy. Overall, the TDM, model ...
Sep 23, 2024 ， This paper examines the integration of Industry 5.0 principles with advanced predictive maintenance (PdM) and condition monitoring (CM) practices, based on ...
1 day ago ， In the bearing fault diagnosis, common deep learning methods mainly include Convolutional Neural Networks (CNN), Deep Belief Network (DBN), Long Short Term ...
Sep 16, 2024 ， This research presents a data-driven approach for detecting faults in industrial machines using sensor data. The method aims to optimize system performance, ...
2 days ago ， This paper presents a comprehensive study on the application of ML algorithms for stress detection, with a focus on physiological and behavioral data analysis.