Sinha Namrata Ieee Access Link Link Access
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Recent advances in deep learning have demonstrated significant potential for automated feature extraction and robust classification in fault diagnosis tasks. Convolutional neural networks (CNNs) can learn hierarchical representations from raw signals or their time–frequency transforms (e.g., spectrograms, scalograms) and have achieved state-of-the-art results in bearing and rotor fault detection. Combining multiple sensor modalities, such as vibration and stator current, further improves diagnostic performance by capturing complementary information: vibration sensors are sensitive to mechanical defects while current signals reflect electromagnetic irregularities caused by faults.
Dr. Namrata Sinha is a prominent researcher in the field of Electrical Engineering. Her work frequently appears in high-impact journals, including IEEE Access . She is best known for her research on the integration of Electric Vehicles (EVs) into the power grid, smart grid technologies, and optimization techniques for power systems. Serving as the direct point of contact if
: Research involving the design and analysis of slanted polarized antennas using inverted resonators. Biomedical & Engineering Research
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