bearing-fault-diagnosis

This paper introduces the Diagnostic Evidence Network (DENet), a multi-task framework that extends AI-based bearing fault diagnosis to produce physically verifiable evidence, such as predicted characteristic frequencies and temporal localization of impulses, while using a QLoRA-adapted language model to generate constrained diagnostic reports that reduce hallucinated content.

Proposes a knowledge-guided two-stage transfer learning framework using a lightweight GPT-2-style Transformer for cross-domain bearing fault diagnosis with limited data, achieving 92.61% accuracy with only 10% labeled data.