In this work, we address these gaps by proposing a hybrid deep learning framework that fuses features from vibration spectrograms and raw current waveforms. Our contributions are:
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Given the variety of professionals named Namrata Sinha, a direct search on IEEE Xplore may not immediately return the intended result. Here is a step-by-step, comprehensive approach to refine your search. In this work, we address these gaps by
In this article, Sinha Namrata analyzed the performance of millimeter-wave communication systems in the presence of interference. The authors derived closed-form expressions for the outage probability and ergodic capacity, and evaluated the impact of interference on system performance. Given the variety of professionals named Namrata Sinha,
Abstract (150–200 words) This paper presents a robust deep learning framework for early detection and classification of faults in three-phase induction motors using vibration and stator-current signals. We design a data-preprocessing pipeline that includes resampling, denoising with wavelet thresholding, and time–frequency feature extraction via short-time Fourier transform (STFT) and continuous wavelet transform (CWT). A convolutional neural network (CNN) processes spectrogram/CWT images while a parallel 1D-CNN processes raw waveform data; features are fused and fed to fully connected layers for multi-class fault classification (bearing defects, rotor bar faults, eccentricity, healthy). We evaluate the model on an industrial testbed and the publicly available CWRU and Paderborn datasets, achieving average accuracy >98%, F1-score >0.97, and robust performance under variable loads and noise. Ablation studies quantify the contribution of each sensor modality and preprocessing step. The proposed method is computationally efficient for edge deployment and includes guidelines for transfer learning to adapt to new motor types.