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.
In the rapidly evolving world of academic publishing, finding a specific author’s work—especially one published in a prestigious open-access journal like IEEE Access —can sometimes feel like searching for a needle in a digital haystack. For researchers, students, and industry professionals tracking contributions in electrical engineering, computer science, and applied technologies, the name has surfaced with notable frequency.
The contributions of researchers like Namrata Sinha to platforms such as IEEE Access are invaluable. They embody the spirit of exploration and innovation that drives human progress. As technology continues to evolve, the work of individuals in STEM fields will play a pivotal role in shaping our future.
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