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arXiv AI By Yuntong Chen, Jianyu Liu, Yingqi Li, Guobin Zhao, Ziang Wang, Chao Chen, Xitian Tian, Lijiang Huang

A Multi-level Information Integration Framework for Physically Verifiable Fault Diagnosis of Rotating Machinery

Read the original on arXiv AI →

The paper proposes a Diagnostic Evidence Network (DENet) that augments traditional fault‑diagnosis outputs with structured evidence, including classification, predicted characteristic frequency, and temporal localization of impulses. This evidence aligns with theoretical bearing physics and can be validated at inference time, achieving high AUROC for misclassification detection without sacrificing accuracy. A QLoRA‑adapted language model then translates DENet’s evidence into maintenance reports, markedly reducing unsupported claims.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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