Applying large language models (LLMs) to industrial fault classification is hindered by the mismatch between tabular sensor data and text-based inputs and by the high memory cost of fine-tuning billion-parameter models on edge hardware. This paper presents TabEng-QLoRA, a framework with three contributions: (1) a criticality-aware serialization module that converts tabular sensor records into structured prompts, placing fault-critical features in semantically prominent positions; (2) a saliency-guided rank allocation mechanism that profiles layer-wise activation norms on a 500-sample calibration set and assigns adapter ranks in three tiers (r ∈ 8, 16, 32) ; and (3) a feed-forward domain router for automatic adapter selection (98. 1% accuracy, 0. 6 ms latency). Experiments on three public benchmarks (the AI4I Predictive Maintenance Dataset) using three foundation models (LLaMA-3-8B, Mistral-7B, and Qwen2-7B) show that TabEng-QLoRA achieves a mean macro F1 of 0. 908, a 10. 6% gain over standard QLoRA, within 4. 6–5. 2 GB peak GPU memory. The framework closes 82% of the gap to full fine-tuning, while offering advantages in cross-equipment transfer learning (zero-shot macro F1: 0. 743 vs. 0. 341 for XGBoost retrained on 20% of target-domain data, as XGBoost cannot perform zero-shot transfer). Ablation results confirm statistically significant contributions from all three components (p < 0. 001).
Toksöz et al. (Sun,) studied this question.