A physics-aware reservoir computing model for motor drive fault diagnosis converts labeled data into AI parameters to achieve higher accuracy and interpretability than black-box methods without extensive retraining.
Uncertainty-aware artificial intelligence for gear fault diagnosis in motor drives
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Quantizing weights and activations in a pre-trained probabilistic BNN for gear fault diagnosis yields 30-45% computational efficiency gains with no loss in accuracy or uncertainty estimates.
citing papers explorer
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Unlocking Embodied Probabilistic Computational Features in Motor Drives
A physics-aware reservoir computing model for motor drive fault diagnosis converts labeled data into AI parameters to achieve higher accuracy and interpretability than black-box methods without extensive retraining.
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Quantized Probabilistic AI for Gear Fault Diagnosis in Motor Drives
Quantizing weights and activations in a pre-trained probabilistic BNN for gear fault diagnosis yields 30-45% computational efficiency gains with no loss in accuracy or uncertainty estimates.