Progressive Knowledge-Guided Large Language Model Framework for Bearing Fault Diagnosis

2026-06-15

AI Summary

This study addresses key challenges in bearing fault diagnosis—namely, the trade-off between global statistical features and local transient signals, insufficient physical interpretability of extracted features, and inefficient multi-source information fusion—by proposing a progressive, physics-guided multi-scale vibration signal processing framework. Integrating bearing kinematic theory with defect characteristic frequencies, the method constructs an 81-dimensional physically traceable feature space and introduces a fault-adaptive signal segmentation mechanism. Structured fault mechanism knowledge is implicitly encoded within a large language model architecture, enabling autonomous multi-scale fusion without external dependencies. Evaluated on four public datasets, the approach achieves 98.49% accuracy while reducing computational cost by 12.6× compared to baseline methods, with feature activations showing strong alignment with established fault mechanisms.

Abstract

Vibration-based bearing fault diagnosis requires resolving three interrelated measurement challenges, including the trade-off between global statistical feature efficiency and local transient signal fidelity, insufficient traceability of measurement features to underlying fault physics, and ineffective multi-source measurement information fusion across diagnostic scales. This paper presents a progressive physics-guided multi-scale vibration signal processing framework that addresses all three challenges within a unified diagnostic pipeline. An 81-dimensional measurement descriptor, derived from bearing kinematic theory and characteristic defect frequencies, establishes a physically traceable feature space enabling real-time fault screening at approximately 20 ms per sample. A fault-adaptive signal segmentation mechanism then directs analytical attention toward fault-relevant waveform regions guided by physics-based priors, without manual feature engineering. Structured fault mechanism knowledge is further encoded implicitly in model parameters during training, enabling autonomous multi-scale measurement fusion without external knowledge dependencies at inference. Validated on four public benchmark datasets under diverse operating conditions, the framework achieves 98.49% diagnostic accuracy with a 12.6-fold reduction in computational cost relative to signal-level baselines. Interpretability analysis confirms that diagnostic feature activations align with established bearing fault mechanics, supporting measurement traceability in safety-critical industrial systems.

Problem

• bearing fault diagnosis
• vibration signal
• feature traceability
• multi-scale fusion
• measurement challenges

Innovation

• physics-guided
• multi-scale fusion
• fault-adaptive segmentation
• traceable feature space
• knowledge-implicit LLM