Cascaded multi-granularity pruning reaches 13.8x compression on MHA+GELU LLMs for bearing fault diagnosis at 83.82% accuracy while causing ~74pp collapse on GQA+SwiGLU models that violate the formalized Structural Independence Assumption.
LLM-based fra ework for earing fault diagnosis,
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A knowledge-guided two-stage Transformer framework achieves 92.61% average accuracy in cross-domain bearing fault diagnosis using only 10% labeled target data on four real-world datasets, outperforming prior methods by 17.24 points.
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Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT
Cascaded multi-granularity pruning reaches 13.8x compression on MHA+GELU LLMs for bearing fault diagnosis at 83.82% accuracy while causing ~74pp collapse on GQA+SwiGLU models that violate the formalized Structural Independence Assumption.
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An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data
A knowledge-guided two-stage Transformer framework achieves 92.61% average accuracy in cross-domain bearing fault diagnosis using only 10% labeled target data on four real-world datasets, outperforming prior methods by 17.24 points.