A High-dimensional Variational Zero-Trust Hopfield Network integrated into the SDN control plane for secure and efficient IIoT communication and demonstrates that the proposed framework provides an efficient, scalable, and secure solution for IIoT-SDN networks under high-load conditions.
G. Senthil, S. Suganthi, R. Deepa· The European Physical Journa...· 0 citations
Accurate defect detection of traction motors is essential for preserving the performance and safety of electric cars and industrial gear. This research presents a onedimensional convolutional neural network (1D-CNN) architecture for the automated identification of faults using vibration and current information obtained from a 150 kW traction motor operating under varying load and speed circumstances. The proposed technique concurrently analyses time-series vibration and current data, allowing the model to detect both mechanical and electrical irregularities. The dataset includes several defect kinds and healthy operating settings, offering a realistic basis for training and assessment. Experimental findings indicate that the 1D-CNN model attains a classification accuracy of 98.7% with just vibration signals, 97.5% with only current inputs, and 99.4% when both modalities are integrated. The precision, recall and F1-score of the integrated signal model are above 99 percent in all types of faults, which shows good performance even in varying operations. The findings underscore the efficacy of multi-signal 1D-CNN architectures for the prompt and precise detection of traction motor faults, reducing dependence on human feature extraction and facilitating predictive maintenance tactics. The proposed method provides a scalable and generalizable solution for practical traction systems, enhancing operating dependability and decreasing maintenance expenses.
M. Indhumathi, R. Deepa, M. Rubinabegam et al.· International Conference on...· 0 citations
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